Bibliographic record
Abstract
The present experiments examined the encoding and retrieval conditions in an item-method-directed forget (IMDF) study that included a novel control condition. In the IMDF condition, half of the items were followed by a remember cue whereas the other half were followed by a forget cue. In a remember-both control condition, half of the items were followed by an item identifier called Set A; whereas the other half of the items were followed by a Set B identifier. At the test, items were recalled as a function of the instruction cue or the set identifier. Across two experiments, directed-forgetting effects and associated benefits were found. Further, results from both studies revealed a new way to demonstrate the benefit of IMDF - directed-forgetting participants made more correct source attributions compared to remember-both participants. These benefits were obtained using a within-subjects IMDF paradigm (Experiment 1) as well as a between-subjects IMDF paradigm (Experiment 2). These patterns of results are consistent with several current theories of item-method-directed forgetting.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".