Bibliographic record
Abstract
Often, philosophers, linguists, and cognitive scientists aiming to construct a theory of meaning for languages focus on how words are used by people to deal with the world. They might assume there is a regular way in which the word dolphin is used - that people regularly use dolphin to refer to or denote a class of aquatic beasts. And they might assume that there is a central, core use of words - using She has a pet dolphin to correctly describe some state of affairs, perhaps. If these assumptions were correct, dolphin might then be defined in terms of some regular function(s) it serves in a community of individuals who "speak the same language," understood as "use words in the same way" (cp. Davidson 1967, Sellars 1974; contrast Chomsky 1996a, b, 2000a, Fodor 1998). These assumptions are built into technical terms: "truth (or correctness) conditions," "functional (or conceptual) role," etc. Because these attempts focus on communities, circumstances, things, and so on, I call them "externalist" approaches.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".