How to find what's in a name: Scrutinizing the optimality of five scoring algorithms for the name‐letter task
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although the name‐letter task (NLT) has become an increasingly popular technique to measure implicit self‐esteem (ISE), researchers have relied on different algorithms to compute NLT scores and the psychometric properties of these differently computed scores have never been thoroughly investigated. Based on 18 independent samples, including 2690 participants, the current research examined the optimality of five scoring algorithms based on the following criteria: reliability; variability in reliability estimates across samples; types of systematic error variance controlled for; systematic production of outliers and shape of the distribution of scores. Overall, an ipsatized version of the original algorithm exhibited the most optimal psychometric properties, which is recommended for future research using the NLT. Copyright © 2009 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it