On the empirical status of the matching law: Comment on McDowell (2013).
Bibliographic record
Abstract
The matching law, regardless of the version, is a mathematical model that accounts for an organism's response rate as a function of the reinforcer rate. McDowell (2013) investigated to which extent a combined version of the quantitative law of effect (Herrnstein, 1970) and the generalized matching law (Baum, 1974) accounts for a substantial amount of the variance through several data sets. Even if I agree with most points raised by McDowell, there are 2 important issues within his reanalysis. Two out of 6 studies relied on pooled-subject data that are inappropriate for an investigation of the matching law (Caron, 2013). Moreover, the combined equation was not systemically investigated through all data sets. The current study casts some doubt on the empirical status of modern matching equations and thus shows that they still deserve extensive attention.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.072 | 0.074 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".