Conceptual Combination, Property Inclusion, and the Aristotelian-Thomistic View of Concepts
Bibliographic record
Abstract
Understanding how properties are extended to combined concepts is critical to theories of concepts. In human judgments, properties true of a noun (ducks have webbed feet) become less true when that noun is modified (baby ducks have webbed feet), while properties false of a noun (candles have teeth) become less false when that noun is modified (purple candles have teeth). These modification and inverse modification effects have been shown to be extremely robust. Gagné and Spalding ( 2011 , 2014b ; Spalding and Gagné 2015 ) have argued that these effects are driven by expectation of contrast. The current experiment shows that, as expected, the modification and inverse modification effects are unaffected by the normative force with which a property is predicated of the head noun, supporting the expected contrast explanation. The results are discussed with respect to an Aristotelian-Thomistic approach to concepts (Spalding and Gagné 2013 ).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".