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
Abstract Thinking machines must be able to use language effectively in communication with humans. It requires from them the ability to generate meaning and transfer this meaning to a communicating partner. Machines must also be able to decode meaning communicated via language. This work is about meaning in the context of building an artificial general intelligent system. It starts with an analysis of the Turing test and some of the main approaches to explain meaning. It then considers the generation of meaning in the human mind and argues that meaning has a dual nature. The quantum component reflects the relationships between objects and the orthogonal quale component the value of these relationships to the self. Both components are necessary, simultaneously, for meaning to exist. This parallel existence permits the formulation of ‘meaning coordinates’ as ordered pairs of quantum and quale strengths. Meaning coordinates represent the contents of meaningful mental states. Spurred by a currently salient meaningful mental state in the speaker, language is used to induce a meaningful mental state in the hearer. Therefore, thinking machines must be able to produce and respond to meaningful mental states in ways similar to their functioning in humans. It is explained how quanta and qualia arise, how they generate meaningful mental states, how these states propagate to produce thought, how they are communicated and interpreted, and how they can be simulated to create thinking machines.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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