Bibliographic record
Abstract
There is a distinction in scientific explanation between the explanandum, statements describing the empirical phenomenon to be explained, and the explanans, statements describing the evidence that allow one to predict that phenomenon. To avoid tautology, these sets of statements must refer to distinct domains. A scientific explanation of semantics must be grounded in explanans that appeal to entities from non-semantic domains. I consider as examples eight candidate domains (including affect, lexical or sub-word co-occurrence, mental simulation, and associative learning) that could ground semantics. Following Wittgenstein (1954), I propose adjudicating between these different domains is difficult because of the reification of a word’s ‘meaning’ as an atomistic unit. If we abandon the idea of the meaning of a word as being an atomistic unit and instead think of word meaning as a set of dynamic and disparate embodied states unified by a shared label, many apparent problems associated with identifying a meaning’s ‘true’ explanans disappear. Semantics can be considered as sets of weighted constraints that are individually sufficient for specifying and labeling a subjectively-recognizable location in the high dimensional state space defined by our neural activity.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".