Productions Need Not Match Study Items to Confer a Production Advantage, But It Helps
Bibliographic record
Abstract
Abstract: The production effect is the finding that, relative to silent reading, producing information at study (e.g., reading aloud) leads to a benefit in memory. In most studies of this effect, individuals are presented with a set of unique items, and they produce a subset of these items (e.g., they are presented with the to-be-remembered target item TABLE and produce table) such that the production is both unique and representative of the target. Across two preregistered experiments, we examined the influence of a production that is unique but that does not match the target (e.g., producing fence to the target TABLE, producing car to the target TREE, and so on). This kind of production also yielded a significant effect—the mismatching production effect—although it was smaller than the standard production effect (i.e., when productions are both unique and representative of their targets) and was detectable only when targets with standard productions were included in the same study phase (i.e., when the type of production was manipulated within participant). We suggest that target-production matching is an important precursor to the production effect and that the kind of production that brings about a benefit depends on the other productions that are present.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".