Temporal Memory Accuracy For Autobiographical Events Across Childhood
Bibliographic record
Abstract
There is relatively little research about how accurately children remember temporal (when) details associated with autobiographical events. This thesis is part of a large-scale study with a goal of providing legal practitioners information about children’s accuracy for several forensically-relevant temporal details (e.g., age during event, month, time of day), and tests whether accuracy is affected by age of the child and valence of the event. Parents nominated and provided timestamped documentation for a positive and negative event (occurring within the past two years). Children (N=121; 4-6-years-old, 7-9-year-olds, 10-12-year-olds,13-15-year-olds) then answered various temporal questions about the events. We found age-related improvements for total temporal accuracy (sum of scores across temporal judgments), age, season and month estimates, and higher accuracy for positive compared to negative events for some temporal judgments. These preliminary findings provide novel insight for memory researchers and legal practitioners and will help with understanding time estimates provided by children.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".