Where do memories for repeated events, single instances, and unique events fall on the semantic-episodic continuum?
Bibliographic record
Abstract
In recent years, there has been a growing interest in a proposed continuum between semantic and episodic memory. Bontkes et al. (2023) have recently used this framework to investigate memories of repeated events. In this study, we are interested in whether memories of unique events, single instances of repeated events, and repeated events fit predictably on the semantic-episodic continuum. We are hypothesizing that all three types of events will draw on both semantic and episodic memory but that unique events will be the most episodic and least semantic while repeated events will be the most semantic and least episodic. Memories of instances of repeated events will fall between unique events and repeated events in their relative reliance on episodic and semantic memory. Bontkes, O., Palombo, D., & Rubínová, E. (2023, August 5). Similarity impacts where repeated events fall on the semantic-episodic continuum. Open Science Framework. https://doi.org/10.31219/osf.io/rw49j
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".