Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Matching behavior is a phenomenon describing response rate ratios of an organism as a function of their associated reinforcer rate ratios. The generalized matching law (GML), its quantitative formulation, has been frequently found to explain over 80% of the variance in concurrent reinforcement schedules. However, a previous paper found by means of Monte Carlo simulations that matching behavior could be due to environmental constraints on behavior rather than a mere decision-making process. The purpose of the current study is to systemically investigate the influence of constraints induced by concurrent schedules of reinforcement. A Monte Carlo simulation was carried out. Results showed that the GML reached much better explained variances with real (and artificial) organisms than the current simulated results. Thus, a learning process seems partly necessary to generate matching behavior. According to the current findings, concurrent reinforcement schedules clearly induced a quantitative dependency between behavior rates and reinforcer rates. The simulation demonstrates that matching behavior is not only a consequence of a behavioral (decision-making) process, but of environmental conditions also.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it