Learnin' 'Bout my Generation: The Effects of Generation on Encoding, Recall, and Metamemory
Bibliographic record
Abstract
My dissertation examined how encoding strategies, recall, and metamemory shift across two study-test experiences. Differential recall of generate targets and read targets on Test 1 led participants to develop an improved encoding strategy for their more poorly recalled target type, thus eliminating differential recall on Test 2 (Experiment 1-3). However, recall also improved across tests for groups that were not tested on both target types on Test 1 (Experiment 2), and for groups that studied and recalled only one target type (Experiment 1). Participants’ reported strategies (Experiment 1) and metamemory judgments (Experiment 3) were used to elucidate how and when people modify their encoding strategies in an effort to improve future memory performance. Overall, the present study confirmed that people can learn about the effectiveness of a study strategy both during studying and on a test, and revealed that this learning is more ubiquitous and varied than previous research suggested.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.018 |
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".