Productions Need Not Match Study Items to Confer a Production Advantage, But It Helps
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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