CARIN theory reanalysis reanalyzed: A comment on Maguire, Devereux, Costello, and Cater (2007).
Bibliographic record
Abstract
P. Maguire, B. Devereux, F. Costello, and A. Cater discussed the Gagné and Shoben (1997) CARIN theory of conceptual combination and, after presenting a sample drawn from the British National Corpus and comparing the two corpora, concluded that the Gagné and Shoben corpus is too small and unrepresentative. They then discussed the mathematical model presented by Gagné and Shoben and claimed that the model does not incorporate relational competition. In this article, the authors present critical aspects of the mathematical model not considered by Maguire et al. and show that the mathematical instantiation of CARIN presented by Gagn and Shoben is, in fact, very sensitive to the number of strong competing relations. The authors then present some new comparisons between the corpora, showing that they correspond surprisingly well.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".