Thread

Rejection Sensitivity in Gifted and Twice-Exceptional Children

102 points · 83 comments · actfrench

bonsai_spool · 1791119993 · focus · HN ↗
One should be careful with vibe-written / vibe-aided articles here. Who knows what is actually supported vs what sounded okay and the author (who is not a clinician) then signed off on.
Even the core idea, 'rejection sensitivity' is acknowledged not to be a diagnostic entity: so then how can we make definitive comparisons? Maybe clinical gestalt... but the author is not trained to see and evalaute patients.
1. adamsb6 · 1791122357 · focus · HN ↗
  Is this all that unusual for the field of education research?
  Vibe writing may be an improvement. I doubt frontier models would go along with whole language reading instruction without a tremendous amount of pushback.
  For those not in the know, a generation of children were taught to read sans phonics. They were told to do things like guess at words based on pictures or context clues. Some lessons had teachers covering words up and asking kids to use guessing strategies at what they might be.
  1. CrazyStat · 1791125020 · focus · HN ↗
    I prompted ChatGPT (paid subscription, latest model, high thinking effort):
    > Give me two paragraphs on the benefits of whole language reading instruction. Don't refer to other reading instruction methods or compare/contrast, just focus on the positives.
    And got, with no pushback,
    > Whole language reading instruction emphasizes making reading meaningful, engaging, and connected to students’ real experiences. By surrounding children with rich, authentic texts—such as stories, poems, informational books, and student-created writing—it encourages them to see reading as a purposeful form of communication rather than simply an academic task. Students are invited to use context, prior knowledge, illustrations, language patterns, and their understanding of meaning to engage with texts. This can help foster curiosity, comprehension, confidence, and a positive identity as a reader. Because students often have opportunities to choose books and respond personally to what they read, whole language instruction can also support motivation and a lasting enjoyment of reading.
    > Whole language instruction also integrates reading with writing, speaking, and listening, helping children experience literacy as an interconnected set of communication skills. Classroom activities may include shared reading, read-alouds, journal writing, storytelling, discussion, and collaborative projects, allowing students to develop literacy in varied and socially meaningful contexts. Teachers can build instruction around students’ interests and developmental needs, creating opportunities for creativity, self-expression, and deeper thinking about texts. This emphasis on authentic language use can strengthen vocabulary, comprehension, oral language, and awareness of how written language functions while helping students develop independence and confidence in using literacy throughout their daily lives.
    Time to update your priors on how sycophantic frontier models are.
    sfRattan · 1791127897 · focus · HN ↗
    It might not obvious to a number of readers on HN, but whole language reading instruction is soundly discredited[1]. For those who prefer listening, the America Public Media podcast Sold a Story[2] covers the history of reading instruction in the English-speaking world and the rise and (slow) fall of whole language hokum in extensive depth.
    And CrazyStat's implication strikes me as still largely true, so I'll make it explicit: even frontier LLMs will still resort to sycophancy when there's sufficient contradictory information in their weights to reproduce only some of it in response to a user query. And the harnesses that LLM providers silently, invisibly integrate behind-the-scenes to counteract hallucination and syncophancy are definitionally patch-jobs at best. The latest models may be better when explicitly instructed or configured by default to search the web and pull sources into their context, but the root problem remains unsolved and can still rear its flattering head if you're not paying close attention.
    [1]: <a href="https://journals.sagepub.com/doi/10.1177/1529100618772271" rel="nofollow">https://journals.sagepub.com/doi/10.1177/1529100618772271
    [2]: <a href="https://features.apmreports.org/sold-a-story/" rel="nofollow">https://features.apmreports.org/sold-a-story/
    antonvs · 1791133470 · focus · HN ↗
    > when there's sufficient contradictory information in their weights
    All this talk of contradictory information, sycophancy, and flattery is little more than anthropomorphic projection in cases like this.
    One prompt will elicit a response based on one set of weights that are most closely related to the prompt. A different prompt will elicit a response from a different set of weights, for the same reason.
    There’s no contradiction from the point of view of the model and its responses, and no need to invoke sycophancy or flattery as a reason for the behavior. You’d get the same behavior from a model that hadn’t been RLHFd to be sycophantic.
    sfRattan · 1791135150 · focus · HN ↗
    > There’s no contradiction from the point of view of the model and its responses, and no need to invoke sycophancy or flattery as a reason for the behavior.
    Sycophancy and flattery are words that relate to how humans experience LLMs. It's appropriate to use words that could anthropomorphize as descriptors for a problem when that problem exists in the space of human experience. It doesn't matter that an LLM is merely a predictive model. It matters that humans who use the model perceive it as a conversation partner and walk away feeling 'flattered' by the model's predictive responses to the humans' input, which the model predicts without regard for factual accuracy, which happens in turn because (as you point out) "there’s no contradiction from the point of view of the model."
    We could call the problem "LLM predictive output which tends to have the effect of flattering the user's opinionated assumptions without regard for factual accuracy" if you prefer that much of a mouthful, but "sycophancy" describes the problem well and is one word instead of twenty.
    > One prompt will elicit a response based on one set of weights that are most closely related to the prompt. A different prompt will elicit a response from a different set of weights, for the same reason.
    Yes or, in other words: there's sufficient contradictory data which were used to train the model's weights that it will predict (a series of tokens which when read in order in English convey) accurate information for some prompts and (a series of tokens which when read in order in English convey) inaccurate information for others. That the contradiction is beyond the LLM's scope is part of the problem. The two identical parentheticals above are (or ought to be) as unneeded as pointing out that a statistical model cannot technically be sycophantic.
    eudamoniac · 1791137636 · focus · HN ↗
    I think it's a priori obvious that that method is idiotic; does the podcast detail how this made it into curricula? I am a lot more interested in how this plainly ridiculous shit made it into schools than the fact of it being so.
    sfRattan · 1791138403 · focus · HN ↗
    Yes, the podcast goes into exactly that detail.
    IIRC it was a teacher in New Zealand[1] who had teacherly charisma, was earnestly committed to her method, and was so profoundly wrong that she became the first domino of a regression in reading ability which we are still feeling today across the English-speaking world. And teachers and education professionals became fans of both the method and the growing personas behind it. There were Americans who turned whole language hokum into a product for sale... using the cult-like sales tactics of Amway and other MLMs but applied to a target audience of largely woman-attended teachers' development conferences and fairs instead of Mormon housewives.
    Sadly, humans were capable of producing plausible sounding nonsense long before the advent of LLMs. And promoting it persuasively.
    [1]: <a href="https://en.wikipedia.org/wiki/Marie_Clay" rel="nofollow">https://en.wikipedia.org/wiki/Marie_Clay
    ndriscoll · 1791142070 · focus · HN ↗
    My understanding is that it originated from observing how fluent readers do things and observing that the most successful, smartest children can actually manage to start reading without much/any explicit phonics instruction. So you could naively look at who ends up reading the best and how did they do it and think that perhaps that makes sense to do for everybody. The problem is that the sorts of techniques that work for outliers are going to be very different from the sorts of techniques that work for the median.
    It wouldn't terribly surprise me if we walk into a similar trap with math. Like when I took a look at some of the common core math, I saw strategies that I use for arithmetic, where like sometimes I might do four-digit multiplications starting from the most significant digit with a sequence of approximations/corrections to arrive at the correct answer. It wouldn't terribly surprise me if this sort of thing just doesn't work for the median person, but from the little I've looked at it, it seems like the effects of CC math are mostly neutral anyway.
    sfRattan · 1791142516 · focus · HN ↗
    I think what those observations missed is that the best readers quickly build up a large baseline vocabulary of known words, whether by phonics or something else, and therefore appear to be guessing often or, when they're guessing, have a much better understanding of the surrounding prose than a median learner who has only been taught to guess at every word.
    And yes, something similar is happening with common core math. Curricula are trying to teach the tricks, shortcuts, and mental re-arranging of numbers that are only possible to comprehend once the basics are memorized and internalized. It was miserable memorizing times tables, but I remember being able to start to see the ways you could combine and recombine numbers after I had successfully memorized them. Trying to teach those things without any memorization is putting the cart before the horse.

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