Is zjudge a better prime for JUDGE than zudge is?: A new evaluation of current orthographic coding models.
Bibliographic record
Abstract
Three masked priming paradigms, the conventional masked priming lexical-decision task (Forster & Davis, 1984), the sandwich priming task (Lupker & Davis, 2009), and the masked priming same-different task (Norris & Kinoshita, 2008), were used to investigate priming for a given target (e.g., JUDGE) from primes created by either adding a letter to the beginning of the target (e.g., zjudge) or replacing the target's initial letter (e.g., zudge). Virtually all models of orthographic coding that allow calculation of orthographic similarity measures predict that zjudge should be the better prime because zjudge contains all the letters in JUDGE in their correct order whereas zudge does not. Nonetheless, Adelman et al.'s (2014) megastudy data indicated no difference in the effectiveness of these two prime types. The present experiments provide additional support for the conclusion of no difference between these two prime types with the only observed difference being a small zudge prime advantage in Experiment 1b (sandwich priming). These results suggest that models of orthographic coding/word recognition may be well served by allowing inconsistent information (e.g., the "z" in both zjudge and zudge indicates that the presented prime is not JUDGE) to be given considerable weight during the orthographic coding/word recognition process. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".