Bibliographic record
Abstract
This paper presents a systematic investigation of how affirmative and polar-opposite items presented either jointly or separately affect yea-saying tendencies. We measure these yea-saying tendencies with item response models that estimate a respondent's tendency to give a "yea"-response that may be unrelated to the target trait. In a re-analysis of the Zhang et al. (PLoS ONE, 11:1-15, 2016) data, we find that yea-saying tendencies depend on whether items are presented as part of a scale that contains affirmative and/or polar-opposite items. Yea-saying tendencies are stronger for affirmative than for polar-opposite items. Moreover, presenting polar-opposite items together with affirmative items creates lower yea-saying tendencies for polar-opposite items than when presented in isolation. IRT models that do not account for these yea-saying effects arrive at a two-dimensional representation of the target trait. These findings demonstrate that the contextual information provided by an item scale can serve as a determinant of differential item functioning.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".