Bibliographic record
Abstract
Campbell and Reynvoet (2009) found that time to name a single-digit target was about 8 ms faster if preceded by a near prime (±1) compared to a far prime (at least ±3) when prime-digit pairs were interleaved with number comparisons (9↑3; name larger) and not when they were interleaved with multiplication problems (9×3; state product). This is consistent with the claim by previous researchers that magnitude comparison can enable a semantic pathway for digit naming whereas number-fact retrieval can inhibit it. To pursue this, the current study compared priming in the context of multiplication production (9×3=?) versus multiplication verification (e.g., 9×3=24, true or false). Multiplication production, but not verification, may inhibit semantic digit naming to reduce naming-related interference with verbal number production. Indeed, semantic priming of digit naming occurred only in verification and not production blocks. This supports the conclusion that multiplication production can inhibit semantic mediation of digit naming, which is enabled in other number processing tasks (e.g., comparison, verification) that do not compete with naming for verbal number production processes.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".