Bibliographic record
Abstract
This article explains different views on concepts, which are among the most fundamental constructs in cognitive science. Michael Dummett argues that nonhuman animals are not capable of full-fledged conceptual thought but only a diminished form of thought, which he calls, proto-thought. Human beings can remove themselves from the moment and can rise above the confined world of current perceptions because of their linguistic abilities. Donald Davidson, a contemporary philosopher, denies that animals are capable of conceptual thought and claim that conceptual content requires a rich inferential network. Donald Davidson made an argument against animals having conceptual thought. Davidson's original formulation of the argument begins with the claim that having a belief requires having the concept of a belief but adds that having the concept of belief requires possession of a natural language. It follows, then, that to have a belief requires facility with natural language. The characterization of the conceptual/nonconceptual distinction that is implicit in Davidson's metacognitive argument is a complex one involving a capacity for belief about beliefs, a concept of belief, and concepts of truth and falsity. Both Robert Brandom and John McDowell argued that conceptual thought requires more than a capacity for detection. They claim that conceptual thought requires the ability to appreciate the reasons that would justify a given concept's application and use, and this, in turn, is inherently a social practice that is dependent on natural language
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".