Bibliographic record
Abstract
Abstract The response set is composed of the group of acceptable responses in a given experimental context. The response set represents the mapping between a given stimulus and the correct response. For example, in a Stroop task, if the instruction is to “name the ink color of the color word” and the ink colors used are red and green, then the response set consists of the responses “red” and “green.” Response set needs to be differentiated from the stimulus set. In the previous example, the response set (i.e., “red” and “green”) and the stimulus set (i.e., the colors red and green) are the same, but this need not be the case. For example, participants could be instructed to respond “green” to the ink color red and “red” to the ink color green. The response set and the stimulus set together form the task set. There are a number of empirical phenomena associated with the study of response set. Major areas of interest include the effect of (1) the relation between stimulus set and response set, (2) response set membership in selective attention tasks, (3) response set size, and (4) switching response sets. Together, the work on response set has increased our understanding of how behavior is both organized and controlled.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.267 | 0.149 |
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".