The Statistical Developments and Applications Reference List
Bibliographic record
Abstract
Meijer, Streiner, Furr, and Sass (2014) recently provided a brief update on the Statistical Developments and Applications (SDA) section of the journal.This article outlined several ways in which we, the editors of the SDA section, hope to advance the section's goals of keeping readers informed about emerging statistical and psychometric procedures.At that time, we noted that we were preparing a reference list of guidelines for implementing and applying techniques frequently used in personality assessment.More specifically, this list presents references that provide new and useful insights about both popular and newly developed statistical procedures and that illustrate important procedures that could be applied to the study and practice of personality assessment.That reference list is now available (see Table 1 or http:// personality.org/publications/resources-for-research/)and is intended to improve the quality of research published in Journal of Personality Assessment (JPA) and of research more broadly.This list also complements the important statistical Table 1.-A reference list for contemporary methods of research in personality assessment.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".