THE COMPETITION‐AMONG‐RELATIONS‐IN‐NOMINALS THEORY OF CONCEPTUAL COMBINATION: IMPLICATIONS FOR STIMULUS CLASS FORMATION AND CLASS EXPANSION
Bibliographic record
Abstract
One way in which new concepts are added to the conceptual system is through conceptual combination. The competition-among-relations-in-nominals (CARIN) theory (Gagné & Shoben, 1997) proposes that conceptual combination involves specifying a thematic relation (e.g., noun MADE OF modifier) to link the constituent concepts (e.g., chocolate and bee). This theory claims that relations have different strengths for various concepts that correspond to how often a modifier and relation have been paired in previous encounters with combined concepts and that this relational knowledge strongly affects the ease with which combined concepts can be formed. A mathematical model that incorporates key claims of the theory is presented, and empirical findings that are relevant to evaluating the CARIN theory are reviewed. The parallels between the CARIN theory and approaches to stimulus class formation are also discussed.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".