Issues with the SIMPLE model: Comment on Brown, Neath, and Chater (2007).
Bibliographic record
Abstract
SIMPLE (G. D. A. Brown, I. Neath, & N. Chater, 2007) attempts to explain data from serial recall and free recall in the same theoretical framework. While it can fit the free-recall serial-position curves that are the cornerstone of the 2-store buffer model, it does not address 2 classic issues in short-term memory research: similarity effects and presentation-rate effects. Similarity effects in free recall led to important work on organization in free recall, whereas similarity effects in serial recall led to the phonological basis of short-term memory. Presentation-rate effects operate quite differently in free and serial recall. The model also does not consider recall order effects or interresponse times in free recall, which may be problematic.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.030 | 0.044 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".