Inverse reference in adults-elementary arithmetic.
Bibliographic record
Abstract
Mauro, LeFevre, and Morris (2003) and Campbell (2008) manipulated problem format to assess university students' simple division and subtraction. Large division problems (dividend > 25; e.g., 42 / 6 = _) and large subtraction problems (minuend > 10; e.g., 13 - 6 = _), but not small problems, were solved more quickly when presented in inverse operation format (e.g., 6 x _ = 42 for division; 6 + _ = 13 for subtraction). They concluded that adults often solve large simple division and subtraction problems by reference to the inverse operation but rely on direct memory retrieval for smaller problems. Their findings, however, might have resulted from unequal practice or mixing of the inverse operations. Here, in Experiment 1 (division) and Experiment 2 (subtraction) normal and inverse formats received equal practice and only one operation was practiced (i.e., division or subtraction). Large divisions and subtractions were solved substantially faster when presented in inverse format, but there was also evidence that subtraction ties (e.g., 12 - 6 = 6) and small subtractions (minuend
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".