MIS – A new scoring method for the operation span task that accounts for Math, remembered Items and Sequence.
Bibliographic record
Abstract
The operation span task is a well-validated measure of the executive component of working memory. Previous scoring systems of this task focus predominantly on the span part of the task, while the distractor – math task – serves as an exclusion criterion for test assessment only. Here, we propose a new Math-Item-Sequence (MIS) system to score performance on the Ospan based on both the span and math part. This new system provides three main improvements: 1) it eliminates the need to introduce arbitrary exclusion thresholds based on performance on the distractor task; 2) it takes into account remembered letters, and their relative position in the sequence separately; 3) it considers performance on the math task in the scoring of the Ospan task as a downweighing factor. In 6 independent samples we show that MIS score correlates highly with previously recommended scoring methods, suggesting that it measures the same underlying concepts. We also show that internal consistency of MIS is very good and comparable to or higher than the previous methods. We argue that MIS could be used in all samples, but might be of particular interest for small samples, where exclusions of participants are especially costly.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".