Bibliographic record
Abstract
ct of this work is thoroughly documented in a recent book edited by Golding and MacLeod (1998). The present article is aimed at bringing together two crucial issues in directed forgetting in one place. The first goal is to compare the impact of the two dominant methods for conducting directed forgetting experiments on several prevalent tests &memory. The second goal is to test whether demand characteristics play any role in directed forgetting effects. Cuing by the ltem Method Versus the List Method From the beginning of work on directed forgetting (e.g., Block, 1971; Muther, 1965), there have been two This research was supported by Natural Sciences and Engineering Research Council of Canada Grant A7459. For her assistance in programming and in collecting the data, I am very grateful to Shelley Hodder. For helpful comments on earlier versions, I thank Barbara Basden, David Basden, David Elmes, and Jonathan Golding. Correspondence should be addressed to C. M. MacLeod, Division of Li
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.040 |
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".