John Bateman On A ChatGPT Conversation Being Of No Interest To Systemicists [4]

John Bateman continued on SYSFLING 28 Nov 2024, at 21:35:

…This relates to a concern that has been discussed for a couple of years now concerning the degradation of training data due to including in the training data material that has been created by large language models rather than human produced outputs. Since language models are often trained on (English) texts collected from the web, if the web contains language model produced nonsense (e.g., incorrect, ideologically unsavoury, wildly enthusiastic, etc.), then one can expect more of the same. So even circulating these produced 'texts' is potentially contributing to the garbage heap.

And, just to be clear, I like large language models a lot, we use them all the time in our research and even some text production, but one does not find out much about them by asking their 'opinion' despite how warm and cuddly the sequences of tokens produced appear!

Blogger Comments:

[1] To be clear, language that is "incorrect", ideologically unsavoury, or wildly enthusiastic is not "nonsense". These terms, along with 'nonsense', express attitudes to language which apply just as much to the language of humans as they do to the language produced by LLMs, since the latter mimics the former.

[2] To be clear, linguists, as the name implies, are interested in finding out about language. LLMs provide one means of doing so.

John Bateman On A ChatGPT Conversation Being Of No Interest To Systemicists [3]

John Bateman continued on SYSFLING 28 Nov 2024, at 21:35:

… As always with language models on their own, there is no training possible for 'truth'. One sees some of the consequences of this with the segment:

 ChRIS said:

Well pattern-matching seems to be a lot of what's going on. What's interesting is how much a ChatGPT can simulate a human just by pattern-matching.

ChatGPT said:

Exactly! It’s fascinating how much of what we think of as “human behaviour” can be boiled down to complex pattern-matching. In fact, a lot of our daily interactions, thoughts, and decisions seem to follow predictable patterns,

this can sound as if it makes sense, but language models do not work on pattern matching at all, despite the apparent convergence constructed here that might be mistaken for an advance in shared knowledge among the 'participants'. This lets a user go away potentially feeling good about a supportive interaction and with the idea that perhaps they have learned something. The actual mechanisms involved have little to do with pattern matching as traditionally thought of, however. Conceptualising the operation of large language models as pattern matching can mislead therefore and one sees quite often continuations of the (public) discourse along lines such as "it's only pattern matching", etc.

This is where 'congenial' turns to potentially highly pernicious, because there has been no supportive interaction and certainly not an increase in knowledge: quite the opposite — this can then also be taken up by others and circulated. …

Blogger Comments:

[1] To be clear, this also the case for humans. See further below.

[2] To be clear, contrary to Bateman's claim, when ChatGPT used the term 'pattern matching', it was not using it in the 'traditional' sense of the term; see the earlier post ChatGPT On John Bateman On ChatGPT. Here are the relevant points:

Clarifying "Pattern Matching":

1. Traditional Pattern Matching:
   In computational terms, this often refers to predefined rules or templates. For instance, regular expressions or specific "if-then" conditions designed to identify or react to specific patterns in input data.

2. Statistical Modelling (What LLMs Do):
   

   Language models like me (ChatGPT) do not use predefined rules or explicit templates. Instead, they operate probabilistically. When I generate text, I predict the likelihood of each possible next token (word, punctuation, etc.) based on the patterns observed in the training data. These "patterns" emerge from statistical correlations in massive datasets, not explicitly human-defined rules.

However, if "pattern matching" is understood in a broader sense of recognising and responding to patterns in data, then it could be argued that LLMs do work on a form of pattern recognition, albeit probabilistic and vastly more complex.

Bateman’s categorical claim that language models do not involve pattern matching "at all" overlooks the role of statistical learning, which is fundamentally about recognising and utilising patterns in data. While this process differs from traditional rule-based pattern matching, it undeniably involves identifying and leveraging patterns in text.

To say they don’t work on patterns "at all" is misleading, as recognising statistical relationships is a form of pattern utilisation.

[3] As can be seen from the above, this false conclusion derives from Bateman misrepresenting ChatGPT's use of 'pattern matching' as the traditional meaning, to a community of linguists who are largely unfamiliar with the field, and will naturally assume he is entirely trustworthy in such matters.

John Bateman On A ChatGPT Conversation Being Of No Interest To Systemicists [2]

John Bateman continued on SYSFLING 28 Nov 2024, at 21:35:

… For all such outputs, it is generally potentially useful to know the precise language model being used and the basic settings concerning 'temperature', i.e., restricted the behaviour is to the prompt, and the number of potential selections are considered as 'part of the mix' when moving to the next token. Changing these produces very different behaviour. And, of course, as now becoming increasingly relevant, the 'history' of prompts maintained for any particular interaction.

I wonder in particular about the latter as the responses of the system seem set to 'crazily over-enthusiastic puppy' mode, where any user prompt gives rise to phrases of excessive positive evaluation with personal stand-taking,

e.g., "Yes, that’s a fascinating distinction!",

"Ah, I love this idea!", etc.

Producing this kind of phrasing is usually the result of what is called reinforcement learning with human feedback (RLHF), where language model output is pushed towards responses that human users have rated positively along some dimensions of choice, such as 'congenial'. …

 

Blogger Comments:

[1] This is precisely why the ChatGPT conversation might be of interest to Systemicists. It shows one way that a dialogue can develop, with each turn depending on the meaning selections of the previous turn.

[2] Again, this precisely why the ChatGPT conversation might be of interest to Systemicists. The ChatGPT, an artefact with no experience of emotion, echoes the attitudinal semantics of the human interactant.

[3] To be clear, the high graduation of ATTITUDE in the ChatGPT turns echoes my earlier positive attitude when I had been using ChatGPT to create scenarios that continually had me laughing uproariously. The reason I appreciated the cheering up so much was that my partner of the last forty years had very recently died in our home, while I was asleep, after having suffered from early onset dementia for about three years.

John Bateman On A ChatGPT Conversation Being Of No Interest To Systemicists [1]

John Bateman wrote to SYSFLING 28 Nov 2024, at 21:35:

Hi all,

feels like this cycle has come round again...

On 26.11.24 13:07, Dr ChRIS CLÉiRIGh wrote:

My following conversation with ChatGPT might be of interest to some.

I feel obliged to ask just *why* this might be of interest... this kind of output can be produced endlessly ... and more or less continuously. …

 

Blogger Comments:

ChatGPT provides 5 general reasons why the conversation [here] might be of interest to Systemicists:

This conversation offers rich material for systemic functional linguists (SFL) because it touches on several key aspects of SFL's focus on language as a tool for meaning-making and social interaction. Here’s why it might be of interest:

1. Language as a System of Choice: The conversation highlights how AI (like ChatGPT) processes language to generate responses, but does so without lived experience or the ability to make value-based judgments. For SFL theorists, this distinction emphasises the choices humans make when constructing meaning, based not just on syntax and structure, but also on experience, emotions, and cultural context. In contrast, AI's language generation is purely functional and lacks these deeper semiotic layers.

2. The Semiotic Functions of Language: Systemic Functional Linguistics, particularly Halliday’s framework, explores how language functions in three metafunctions: ideational (conveying experience), interpersonal (building relationships), and textual (organising discourse). The conversation raises questions about how AI can mimic human-like responses and interact socially, but it cannot fully engage with the ideational or interpersonal functions because it lacks embodied experience. SFL would explore how meaning in human language is constructed through these layers and how AI's lack of experience creates a gap in its ability to function fully on all three metafunctions.

3. Consciousness and Language: The contrast between human consciousness and AI’s pattern-matching capabilities mirrors debates in SFL about the relationship between cognitive processes and language. Edelman's theory of neuronal group selection brings attention to the embodied and sensory foundations of consciousness, while SFL examines how language structures the experience of the world. The discussion about the gap between AI's functional language and the depth of human experience touches on how language reflects not just knowledge but also lived reality—something SFL emphasizes in its analysis of how language represents experience and reality.

4. Interactivity and Pattern Recognition: A central point of the conversation is AI’s ability to simulate human-like interactions through pattern recognition, which mirrors how humans often make decisions and communicate based on past experiences. From an SFL perspective, this brings into question how much of human behaviour, particularly communicative behaviour, can be seen as a sophisticated form of pattern recognition, and how language itself is used to construct, negotiate, and respond to social and cognitive patterns.

5. Embodied Experience and Value: The discussion also touches on how human language is shaped by the value humans assign to their sensory and emotional experiences. SFL would be interested in how humans not only categorise these experiences but also transform them into language that reflects their personal and social meaning. Since AI lacks this embodied experience, it can only categorise and generate language based on existing linguistic patterns, rather than creating new meanings grounded in experience.

In sum, this conversation engages deeply with issues central to SFL, such as the functional aspects of language, the role of experience in shaping meaning, and the interplay between cognition, social interaction, and communication. Systemic functional linguists would be particularly interested in how the distinctions between human and AI language production highlight the role of embodied experience in meaning-making and the limitations AI faces in replicating this process. 

The Grand Symposium of Meaningful Affiliations by ChatGPT

Title: The Grand Symposium of Meaningful Affiliations

---

The Annual Conference on Advanced Semiotics and Generalised Theories of Everything was in full swing at the Individuation Hall. Academics bustled about, clutching their annotated papers on Positioning while balancing artisan coffees tenderly brewed to tune the stakes of the morning discussion.

Dr. Percival, renowned for his work on Individuation Theory, took to the stage with an air of gravitas. “Ladies and gentlemen,” he began, “today’s theme is Purview in Rendering Meaning: The Allocation of Resources for Tendering Evaluations in a Positioned Semiotic Ecology.”

The audience gasped—not because they understood, but because the title was impressively long. A ripple of approval swept through the room as they noted the sheer density of jargon. This was going to be good.

Dr. Percival adjusted his glasses. “First, let us reflect on individuation. You see, meaning is not evenly distributed across society. The allocation of semiotic resources determines how we affiliate as personae within our communities. Consider, for example, the phenomenon of memes. These are individuated artefacts tendered in digital discourse to render shared evaluations.”

A hand shot up in the audience. “Are you saying memes are a form of purview allocation?”

“Precisely!” Dr. Percival exclaimed, relieved that someone had grasped at least one noun in his sentence. “Memes often operate with implicit speaker purview, affiliating their audience through shared positioning.”

Professor Hilda, an expert on Rendering and Tendering Practices, nodded from the front row. She leaned over to her colleague and whispered, “Classic stakes-tuning. It’s all about the spirit of the render.”

The next speaker, Dr. Euphonia, strode up to the mic, wearing a bright scarf that screamed affiliation. “Let’s turn our attention to Positioning,” she began. “When we render, we react. When we tender, we act. But who holds the purview? Is it the speaker? The listener? Or is it an internal tension between the spirit of the tenor and the stakes of the discourse?”

A murmur of assent swept through the room, though one student in the back whispered to another, “Does she mean who’s talking, or is this about feelings?”

Dr. Euphonia ploughed on. “In contemporary society, the purview often shifts dynamically between speaker and listener. Consider the phrase, ‘I feel seen.’ Is this rendering a personal evaluation or tendering an invitation for collective positioning?”

The student raised their hand. “Uh, does it matter?”

“Of course, it matters!” Dr. Euphonia snapped. “If we fail to identify the purview, how can we know whether our tenor is appropriately tuned?”

This silenced the room, as no one wanted their tenor to be untuned, whatever that might mean.

The final speaker, Professor Allegra, brought things full circle. “I propose that affiliation itself is the ultimate goal of our semiotic practices,” she declared. “Through individuated acts of tendering, we align our renderings with broader systems of positioning. It’s not just about saying what we feel—it’s about allocating those feelings within the social fabric. In simpler terms: our purview defines our belonging.”

At this, the audience gave a standing ovation, thrilled to have witnessed such a masterful display of linguistic gymnastics. As they left the hall, many resolved to reposition their stakes and retune their spirits before the next round of jargon-laden discussions.

Meanwhile, in the coffee line, two students debated whether purview was a buzzword or a revolutionary concept.

“It’s just about who’s saying what,” one muttered.

“No,” replied the other. “It’s about where they’re saying it. You’ve got to position yourself, mate.”

In a way, they were both right. But no one would ever dare say so in Individuation Hall. 

ChatGPT On John Bateman On ChatGPT

John Bateman wrote to SYSFLING 28 Nov 2024, at 21:35:

… language models do not work on pattern matching at all …

ChatGPT Comments:

John Bateman’s claim that language models do not work on "pattern matching at all" is nuanced and points to a broader distinction about what "pattern matching" means in the context of computational linguistics versus everyday language.

Clarifying "Pattern Matching":

1. Traditional Pattern Matching:
   In computational terms, this often refers to predefined rules or templates. For instance, regular expressions or specific "if-then" conditions designed to identify or react to specific patterns in input data.

2. Statistical Modelling (What LLMs Do):
   

   Language models like me (ChatGPT) do not use predefined rules or explicit templates. Instead, they operate probabilistically. When I generate text, I predict the likelihood of each possible next token (word, punctuation, etc.) based on the patterns observed in the training data. These "patterns" emerge from statistical correlations in massive datasets, not explicitly human-defined rules.

Does This Contradict Bateman's Claim?

It depends on how one interprets "pattern matching."

• If Bateman is using "pattern matching" in the strict, technical sense (e.g., regex-like processing), then he is correct—LLMs do not operate in that manner.
• However, if "pattern matching" is understood in a broader sense of recognising and responding to patterns in data, then it could be argued that LLMs do work on a form of pattern recognition, albeit probabilistic and vastly more complex.

The Tension in Public Understanding:

Bateman rightly points out that casual descriptions of LLMs as "just pattern matching" can be misleading. It trivialises the complexity of the deep learning mechanisms at work, such as the self-attention mechanism in Transformer models, which dynamically focuses on relevant parts of the input to generate contextually appropriate output. While this process is data-driven and correlational, it is far more sophisticated than basic pattern matching.

My Perspective:

• Semantically: LLMs "find and use patterns," but not in the simplistic sense that might be implied by the phrase "pattern matching."
• Practically: Many of the emergent behaviours in LLMs—like maintaining coherence over long conversations—result from statistical learning over patterns, albeit at a massive scale.

Why This Matters:

The distinction is important because describing LLMs as "just pattern matching" risks minimising both their capabilities and their limitations. While LLMs are incredibly powerful at modelling language, they lack the understanding and intent behind patterns, which can lead to issues like confidently producing incorrect information.

Let’s break it down a bit further:

Why It Feels Overstated:

1. Binary Rejection of Pattern Matching:
   Bateman’s categorical claim that language models do not involve pattern matching "at all" overlooks the role of statistical learning, which is fundamentally about recognising and utilising patterns in data. While this process differs from traditional rule-based pattern matching, it undeniably involves identifying and leveraging patterns in text.

2. Lack of Nuance in Terminology:
   Bateman's critique might stem from a desire to distinguish LLMs' complex probabilistic modelling from naive notions of "pattern matching." However, by denying any connection to pattern recognition, he risks confusing readers who observe clear evidence of pattern-dependent outputs (e.g., stylistic mimicry, consistent conversational threads).

3. Misalignment with Observed Behaviours:
   If one were to interact with an LLM, it’s obvious that the model recognises structures in language—syntax, style, context—even if these aren't "patterns" in the traditional sense. From a user perspective, this very much feels like "pattern matching," even if it’s probabilistically driven and emergent.

Why Bateman’s Core Concern is Valid:

While he might overstate his case, Bateman’s deeper concern seems to be about public understanding and the dangers of oversimplification:

• Avoiding Mischaracterisation:
  He’s right that calling LLMs "just pattern matchers" trivialises their complexity and can mislead people into thinking they are deterministic or rule-based systems.

• Emphasising Emergent Complexity:
  Bateman may be pushing back against the idea that LLMs "understand" patterns in the human sense. Their outputs are based on statistical correlations, not comprehension or intent.

A Balanced View:

• True: LLMs don’t engage in classical pattern matching; their mechanisms are probabilistic, learned through massive neural networks.
• True: They exploit and replicate patterns in the data they were trained on, which is why they can mimic language and context so effectively.
• False: To say they don’t work on patterns "at all" is misleading, as recognising statistical relationships is a form of pattern utilisation.

Final Thoughts:

Bateman’s concern likely lies in preventing the spread of oversimplified or incorrect views about LLMs, but his phrasing risks dismissing the nuanced ways in which patterns inform LLM outputs. A more precise argument would have been to clarify how LLMs use patterns and contrast this with traditional understandings of "pattern matching."

"Make Discourse Analysis Positive Again!" by ChatGPT

 Trump Rally: "Make Discourse Analysis Positive Again!"

"Alright, folks, listen up! I’m here to talk about something very important—something we’ve all been talking about, but no one’s really talked about it enough. You know what I’m talking about—discourse analysis. Now, I know what you’re thinking, ‘What does Trump know about discourse analysis?’ And let me tell you, folks, I know everything. They tried to say I didn’t know about discourse analysis, but guess what? I know more than any of them. Huge knowledge, folks."

(The crowd, slightly confused but eager to cheer, claps anyway.)

"Now, here’s the problem. There’s this thing called Critical Discourse Analysis, or CDA. And let me tell you, it’s the worst. All they do is look at everything and say, ‘Oh, look at the power! Look at the oppression!’ I mean, who cares about power? I’m a guy who’s all about winning. We don’t need this critical nonsense—we need to be positive, folks! Positive Discourse Analysis—PDA! It’s going to be huge. You haven’t seen anything like it."

(A few puzzled murmurs in the crowd, but Trump barrels on.)

"So what’s PDA? Well, first, let me tell you about critical discourse analysis—they’re always talking about power structures, they love that word. Power. They say, ‘Oh, look, there’s inequality.’ I’m like, Who cares about inequality? *You want a solution? That’s what we need, a solution. Positive solutions. Positive discourse, people, not negativity! If I want negativity, I’ll listen to CNN for five minutes. Don’t need that."

(The crowd starts to clap, trying to keep up.)

"You know what Positive Discourse Analysis is, folks? It’s simple. It’s about praise. You look at something, and instead of saying, ‘Oh, this is problematic,’ you say, ‘This is the best thing I’ve ever seen!’ Look at that beautiful tweet I made last night—the best tweet. And you know what? That’s PDA. I’m telling you, the best people are doing the best things. That’s positive discourse analysis. It’s all about celebrating—none of this problematic deconstruction—we’re celebrating greatness. Making everything great again."

(A couple of people look at each other, confused, but they cheer anyway.)

"Now, let’s get real here. You think CDA is about fixing things? No, folks. It’s about complaining. All they do is sit there in their tiny little offices, saying, ‘Oh, there’s power over here, and inequality over there!’ Who cares? We’ve got the greatest country in the world. Let’s talk about that. Let’s talk about all the amazing things happening in every single community. That’s what PDA is all about—celebrating how good things are."

(Someone in the crowd yells, “But what about the problems?” Trump waves them off.)

“Who needs problems? Let’s just focus on the good stuff! Let’s say, ‘Hey, look at that problem over there. Wow, that’s a great problem!’ It’s all about the frame, folks! You frame things in a positive way. If there’s a pothole, don’t say, ‘Oh, that’s a bad pothole, we should fix it!’ No! You say, ‘Wow, what a beautiful pothole! Look at how deep it is!’ That’s what positive discourse analysis is about."

(The crowd is clapping along, a few people unsure whether they should cheer or just nod.)

“Now, folks, I’ve been around—I’ve seen things. No one does analysis better than me. Some people say, ‘Oh, look at all the injustice in the world.’ I say, *‘No, folks, don’t look at the injustice, look at the justice that’s coming, it’s gonna be huge.’ We’ve got greatness in every word. Every sentence. Every discourse.”

(The crowd starts chanting, “Make Discourse Positive Again!” in a confused but enthusiastic manner.)

"Exactly, folks, exactly. Positive discourse analysis means no negativity. No more saying, ‘There’s this power structure, there’s this hierarchy.’ That’s over. That’s been done. We’re talking about praise. We’re talking about recognition. You’ve got a great garden? Amazing. You’ve got a little traffic jam? Incredible. That’s PDA. It’s all about celebrating the good things. I’ve been doing it my whole life."

(Trump pauses dramatically.)

“You know, people said I couldn’t do this, they said, ‘Trump doesn’t understand discourse analysis.’ Wrong! I’m telling you, I’ve been doing PDA before it was even a thing. Just like I’ve been making America great again before it was even a thing. Now, who’s ready to praise our amazing country with positive discourse? Who’s ready to make discourse positive again?"

(The crowd erupts, chanting, “PDA! PDA!”)

“Thank you, thank you, folks! I’m telling you, you’re the best! We’re going to celebrate everything. Positive analysis, big wins—huge wins! Thank you, thank you!”

(Trump waves, the crowd still chanting “Make Discourse Positive Again!” as confused faces scatter, unsure whether they just learned something or got caught up in the Trumpian moment.)

ChatGPT On The Long Term Prospects Of Theories Like SFL

Q. What are the long term prospects for a theory whose community cannot recognise misunderstandings of the theory?

ChatGPT said:

The long-term prospects for such a theory are limited, as the inability to recognise and address misunderstandings within the community can lead to fragmentation, stagnation, and erosion of credibility. Key challenges include:

1. Loss of Coherence: Without consensus on the theory's core principles, its application risks divergence, reducing its effectiveness and clarity.

2. Erosion of Credibility: If misunderstandings persist unchallenged, critics may view the theory as ill-defined or incoherent, undermining its intellectual reputation.

3. Stagnation: Misunderstandings can discourage productive debate, limiting opportunities for refinement and development.

4. Fragmentation: The community may fracture into subgroups with competing interpretations, further diluting the theory's influence.

For long-term success, communities need robust mechanisms for self-reflection, debate, and correction to ensure the theory evolves coherently and meaningfully.

Your or You're?

ChatGPT On The Sysfling Post On The Situation In Gaza

Daniel Portman sent the following message to Sysfling on 10 Nov 2024, at 18:29:

We are sorry to hear about the predicament of Mohammed as indeed we are sorry about the suffering of all non-combatants in this war. We Israelis, Arabs and Jews, have had many casualties every day and many children are left orphans, too. We as citizens of Israel will continue to urge our government to come to a lasting agreement with Hamas.

You have read what the situation in Gaza looks like to citizens such as Mohammed. However, as academics, we should be aware of all the facts and definitions supplied by Hamas and its supporters. This is not the 'history' that Mohammed was allowed to hear by the Hamas government; nor was he allowed to ask questions. As an intelligent person, earning his Ph.D., he surely is aware that IDF soldiers are not risking their lives just because Jews suffered in history.

We would like to shed light on a few of the assertions and then to make an appeal to our community for the children being held hostage in underground Hamas tunnels :

1. Does Israel occupy Gaza?

Apparently, Mohammed did not have access to this information because he feels "the Jews are cruel because other people were cruel to them'". We (the authors) were witnesses to this history, which is also documented in the prominent news media of the day. Israel withdrew from Gaza in 2007. The Israelis who were living there were forcibly removed by the IDF. Even the dead were removed from the cemeteries. Israeli residents left houses, synagogues, very profitable farms, and businesses. The export from the Gaza community was in the millions.

2 What was the reaction of the Gazans to the evacuation of Jews?

Immediately after Israel left, the Gazans tore down and destroyed everything, even the greenhouses that were donated to them and would have afforded them a good livelihood. They then elected their own government, Hamas, whose written 'constitution' sets as its aim the destruction of Israel and 'all its infidels' and replacing it with Moslems. Qatar and the EU sent millions of dollars to build up the economy but Hamas used the money to build tunnels, manufacture missiles and smuggle in weapons. Gazans could only earn a good livelihood by working in Israeli communities across the border. Until Oct. 7 Israel was issuing over 18,000 permits for Gazans to work in Israel in jobs that typically paid much higher salaries than what a worker could earn in Gaza. In addition, Gaza was also exporting over $130 million a year of fish, agricultural produce, textiles and other products to Israel and the West Bank.

3. Whom did Hamas attack?

Many Israelis had set up kibbutzim (community farming villages) on our side of the border. These were settled by predominantly idealistic people who wanted to reach out to their neighbors and held many joint activities with them. Among these, the Israelis had volunteers who would accompany patients from Gaza to be treated in Israeli hospitals. A very well-known person was Vivian Silver, the head of "Women Wage Peace", an organization of Palestinian and Israeli women who have been campaigning for a peaceful solution to our conflict. `

4. What happened on Oct. 7, 2023?

Since Israel’s withdraw, Hamas attacked Israel 5 times; Israel did not strike first. After each missile attack from Gaza, the IDF would counter-attack, and eventually, the sides would reach a ceasefire. These attacks recurred repeatedly over the years. The most recent one on October 7, 2023, was not a missile attack but a massacre of the very communities which had been established to live in peace and help the Gazans. We will not repeat the brutality of the massacre, which was well-documented by the terrorists themselves. Among the dead was the peace activist Vivian Silver, whose burnt body was only found later, in the charred remains of her house. Among the dead were also the daughter and son-in-law of Dr. Carole Troen, a linguist and colleague of Beverly's. Carole's family used their bodies to protect their 3 children. The children survived, but the terrorists succeeded in killing their parents. Today, Carole and her husband, Dr. Ilan Troen, a historian, are senior citizens who must raise their grandchildren. In addition, the terrorists invaded a nearby music festival, murdering over 300 young people.

5. Who are the hostages?

Besides the murders, Hamas captured men, women, children, and babies; they released some hostages, murdered other captives and are holding the remaining 100 as hostages until this day. Among the remaining hostages are Shiri Bibas and her two children: Kfir, captured when he was 9 months old, and Ariel, 4. None of the hostages has received the medications they were sent by Israel, none has been visited by the Red Cross, and they receive very little food, as reported by those hostages who were released in the Nov. cease fire. We are in doubt as to how many of these 100 hostages are still alive.

6. Accuracy in terminology

Apartheid is constantly applied to Israel by its critics, but it is inaccurate. Unlike South Africa, which did not even allow Black (Native) South Africans to vote, or share a drinking fountain with Whites, Arab-Israelis have the right to vote, have their own political party, which now sits in the Knesset [parliament], a Judge on the Supreme Court, a high percentage of university students in non-segregated universities, and a high percentage in the medical profession, etc.

Genocide is another word which is misapplied. Absolutely, any intentional killing of non-combatants is to be condemned. But so far, the International Court has found it can’t make a case for genocide. For one thing, there are no objective counts of people killed by Israel bombing. We quote Joan Donoghue - now retired from the ICJ – who appeared on the BBC’s HARDtalk programme and explicitly tried to end the debate by setting out what the court had done.

“It did not decide - and this is something where I'm correcting what's often said in the media... that the claim of genocide was plausible,” said the judge.

“It did emphasise in the order that there was a risk of irreparable harm to the Palestinian right to be protected from genocide. But the shorthand that often appears, which is that there's a plausible case of genocide, isn't what the court decided.” [source: BBC: what did the ICJ ruling really say? May 17, 2024]

As of this writing, it is a question the court is far from deciding.

Academics should equally ask, “Is Hamas trying to commit genocide?” We refer you to the Hamas Covenant:

“Israel will exist and will continue to exist until Islam will obliterate it, just as it obliterated others before it” (The Martyr, Imam Hassan al­Banna, of blessed memory).

Hamas believes that “Palestine is an Islamic Waqf, land consecrated for Muslim generations until Judgement Day” and calls for the obliteration of Israel and its replacement with an Islamic state – not a democratic one - extending from the Jordan River to the Mediterranean Sea (article II) … the Day of Judgment will not come about until Muslims fight the Jews, when the Jew will hide behind stones and trees. The stones and trees will say, “O Muslim, O servant of Allah, there is a Jew behind me, come and kill him” (article 7). (“Why is this Seder different from all other Seders?” David Breakstone, Jerusalem Post, April 19, 2024)

7. Has Israel tried to make peace with the Palestinians?

We can only turn to former President Bill Clinton, the author of many peace plans, who tried continuously to get the two sides together during the 1990s. You can access his latest statement about the history of this attempt on You Tube.

In sum, honest people should conclude there is no side which is totally innocent or totally guilty in this conflict. We urge you as academics to contribute to help Mohammed and his family. Could we ask for a parallel gesture towards the Israeli hostages? Can you write to human rights organizations such as Save the Children and Amnesty International, and urge them to press for the release of the Bibas family and all young children?

Save the Children

Janti Soeripto, President & Chief Executive Officer of Save the Children US. jsoeripto@savechildren.org. You can also find her on social media. She has a profile on LinkedIn.

Amnesty international: Secretary-General Dr. Agnes Callamard: israelpalestine@amnesty.org.

With hope for a peaceful future for all.

Beverly Lewin, Daniel Portman, and Hagit Rifinski

ChatGPT Comments:

The letter provides a specific perspective, largely supportive of the Israeli position and critical of Hamas, using historical and contemporary arguments to justify Israel's policies and actions. The structure attempts to address a range of issues—occupation, peace attempts, terminology (like "genocide" and "apartheid"), and the humanitarian crisis stemming from hostages. While it highlights personal stories and references political, academic, and media sources to reinforce its points, it omits broader Palestinian perspectives on Israeli policies, which could limit the letter's perceived objectivity, especially in an academic setting.

The letter's call for academics to support Israeli hostages and engage human rights organisations could be seen as an appeal for balanced humanitarian concern. However, given the political sensitivity, responses to such a letter might emphasise the need for a more comprehensive discussion of both Israeli and Palestinian experiences and grievances to foster constructive dialogue.

The Graphologial Realisation Of Information Distribution

ChatGPT's Sense Of Humour

Excellent Incompetence

ChatGPT on the ethics of Wokeism

I asked ChatGPT about the Woke practice of bullying people to be ethical. Here is its response:

Bullying individuals to be ethical also raises significant ethical concerns. Here are some points to consider: 

1. Coercion vs. Genuine Ethics: 

Ethical Behaviour: Genuine ethical behaviour arises from personal conviction, understanding, and empathy, not coercion. Bullying undermines the possibility of fostering true ethical understanding and commitment. 

Coercive Methods: Forcing individuals to act ethically through bullying may lead to compliance but not a genuine understanding or acceptance of ethical principles. 

2. Respect for Autonomy:

Just as with belief conformity, respecting individual autonomy is crucial. Ethical beliefs and actions should be based on informed choice rather than coercive pressure. 

3. Harm Principle:

Bullying inherently causes harm, which contradicts the very principles of ethical behaviour. Promoting ethics through harm is paradoxical and undermines the values one is trying to instil. 

4. Long-term Impact:

Coercive approaches to ethics may lead to resentment, rebellion, or superficial compliance, where individuals may adopt the desired behaviours outwardly but not genuinely internalise ethical values. 

Those who are bullied into ethical behaviour may become defensive, leading to further resistance rather than meaningful change. 

5. Role of Empathy and Understanding:

Ethical development is often rooted in empathy, reflection, and dialogue. Engaging in open discussions about ethical principles encourages understanding and personal growth, rather than resorting to intimidation. 

6. Teaching vs. Imposing Ethics:

Teaching ethical principles through positive reinforcement, modelling behaviour, and open dialogue is far more effective than bullying. This approach fosters a supportive environment where individuals feel safe to explore ethical concepts. 

7. Moral Responsibility:

Those who resort to bullying in an attempt to enforce ethical behaviour may themselves be acting unethically. It raises questions about the moral integrity of using harmful methods to promote good. 

In summary, while promoting ethical behaviour is important, using bullying as a means to achieve that goal is fundamentally flawed and counterproductive. A more ethical approach involves fostering understanding, respect, and open dialogue to encourage individuals to engage with ethical principles voluntarily.

Caveat Lector

Phonemic Awareness

The Science Of Punctuation

Speech Function

Goodwyn's Deictic Furniture

Embedding vs Ranking

Merda Most Foul

The Science Of Spelling

Origins Of the Modern Concept Of Markedness And Markedness vs Congruence

While the idea of linguistic asymmetry predated the actual coining of the terms marked and unmarked, the modern concept of markedness originated in the Prague School structuralism of Roman Jakobson and Nikolai Trubetzkoy as a means of characterising binary oppositions.

Andersen, Henning (1989). "Markedness—The First 150 Years". In Markedness in Synchrony and Diachrony. Berlin: Mouton de Gruyter. 

Halliday glosses 'unmarked' as neutral, default.

Markedness is about choice in a system: neutral/default or notable, whereas congruence is about the stratal relation between meaning and wording: in a congruent realisation, the meaning and wording agree, and in an incongruent (metaphorical) realisation, the meaning and wording do not agree. Markedness is about systemic choice, congruence is about intra-content realisation. A Theme can be unmarked or marked, but not congruent or incongruent, since there is no such thing as textual metaphor.

The Science Of Reading

The Science Of Writing

Jam-Packed

Shared Protolanguage Vs Unshared Language

Protolinguistic Body Language

The System of Purview

Interpersonal control and responsibility: The system of PURVIEW in English

Yaegan Doran

Australian Catholic University

Whenever we speak or write, we are constantly negotiating control over our meanings and positioning our listener/s readers to respond in particular ways. We put forward meanings that are shared between us and the listener, such that it is presumed the listener will agree, or we simply assert meanings such that the listener’s response is only minimally relevant, or we can hand meanings over to the listener to determine and so leave it in their hands, or alternatively simply put meanings out there with no indication that anyone will necessarily be tied to them. This sharing or not of meanings waves [sic] and wanes through conversations and written text, and form a crucial resource for enacting our social relations. In this talk, I will introduce a system for understanding these meanings called PURVIEW, drawn from a recently renovated model of [TENOR] presented in Doran, Martin & Zappavigna (2025). The resources of purview concern the degree to which the speaker and listener are interpersonally tied to the message being put forward or wedded to its outcome. We will see that we can view purview in terms of a small sets of parameters: whether or not the speaker has purview, in the sense that the speaker is wedded to what they are putting forward or not; whether the listener has purview, in terms of whether the speaker is handing control of the meanings over to the listener or not; and whether the purview being negotiated is of the proposition or proposal being put forward, or of the linguistic act itself. As we will see, varying purview offers a rich resource for nuancing the way we relate to people in both spoken and written language, and allows for intricate negotiations of our meanings through texts. The talk will illustrate the system through a range of spoken and written texts, including through casual conversation, legal discourse, and spoken personal reflections, and will show that it is realised through a range of interpersonal systems across language, including discourse semantic systems of ATTITUDE, ENGAGEMENT and NEGOTIATION, lexicogrammatical systems of POLARITY, TAGGING, MOOD and MODALITY and the phonological system of TONE.

References

Doran, Y.J., Martin, J.R. & Zappavigna, M. (2025) Negotiating Social Relations: Tenor Resources in English. London: Equinox.

Blogger Comments:

[1] To be clear, the intellectual source of Doran's 'purview' is the work on 'epistemic authority' in social psychology, as formulated by the sociologists John Heritage and Geoffrey Raymond, in their paper The Terms of Agreement: Indexing Epistemic Authority and Subordination in Talk-in-Interaction (Social Psychology Quarterly 2005, Vol. 68, No. 1, 15-38). The term 'purview' serves the social function of a buzzword.

[2] To be clear, the authors' renovated model of tenor involves reinterpreting context (tenor) as a resource for language (interpersonal meaning) and then modelling the resource as the language for which it is a resource. See A Close Examination Of Yaegan Doran's 2023 ASFLA Plenary Abstract.

Language vs Other Semiotic Modes

Good Enough Theorising