Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
On its own, a neuron firing has no meaning, no symbolic quality whatsoever ... It is a level shift as drastic as that between molecules and gases that takes place when thought emerges from billions of in-themselves-meaningless neural firings. (Hofstadter, 1985, p. 649) Virtual reality experiences are making people physically ill. Recently there have been reports of people having flashback experiences, similar to those associated with LSD. One explanation that has been offered is that when people are harnessed to a virtual reality device, the brain receives visual and auditory signals indicating the body is in motion but it is not receiving the kinesthetic signals that normally go with them. Accordingly the brain starts adapting to a new environment by establishing new neural pathways, which can then be activated by other signals, thus producing the flashback phenomena. I have no idea how this conjecture will fare, but the way it is formed is most instructive for thinking about the mind in relation to the brain. Note that it is the brain, not the mind, that `expects' kinesthetic sensations of motion and that starts creating new structures when they do not appear. The mind, in one way, is not fooled. We know we are sitting in a room and not behind the wheel of a racing car roaring around a speedway. In another way, the mind is' fooled whereas the brain is not. We experience the bodily sensations of movement along with the visual and auditory ones, and so we are unaware of the inconsistencies or error signals that our brains are busy trying to rectify. To describe and make sense of such phenomena, therefore, we need a concept of mind as well as a concept of brain. But the two conceptions ought to be in some accord. This article is about two different models of mind, which have different implications for how the brain relates to mind and knowledge. According to one model, knowledge is encoded in the brain in something like the way that data are encoded in a computer's memory. This model is fully consistent with the folk notion of the mind as a container (Lakoff & Johnson, 1980), and so it feels comfortable and seems intuitively compelling; but it starts to become less plausible when we begin to trace out its implications at the brain level. According to the other model, the brain does not actually contain knowledge in any sense that we readily conceive of. Thus this model is radically at variance with folk theory and not even comprehensible until we get a handle on how a brain thus constituted could sustain knowledgeable, intelligent behaviour. However it is this second model that I believe we need to develop in order to have a theory of mind that will carry education into the knowledge age. Computational models of mind-as-container I wonder whether the pioneers of artificial intelligence ever asked themselves whether they should adopt the mind-as-container metaphor. I doubt it. Simulating human cognition on a computer means writing a program. A program consists of instructions, and these instructions apply to the contents of locations in the computer's memory. Thus, right from the start, we have something that closely resembles the folk conception of mind. There is a container (computer memory) with specified objects in it. These objects are of two kinds: beliefs (data) and rules (instructions). To simulate human cognition, all you have to do is load in data that represent human beliefs, along with instructions that represent the rules human beings follow in operating on those beliefs. Beliefs can be represented as propositions (e.g. `All birds are bipeds', `Some evangelists are lechers', `All evangelists are bipeds'). Rules can take the form of logical operators--if-then statements, which have come to be known as `productions'. Of course, what must be represented are not ideal beliefs and rules, but rather those that actually appear in human cognition. Thus, to simulate human reasoning, you do not want a flawless logic machine; but you also do not want one that will infer from the preceding propositions that some birds are lechers or that some evangelists are birds. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it