Implications of the syntheses on definition, theory, and methods conducted by the Response Shift – in Sync Working Group
Bibliographic record
Abstract
PURPOSE: Our aim is to advance response shift research by explicating the implications of published syntheses by the Response Shift - in Sync Working Group in an integrative way and suggesting ways for improving the quality of future response shift studies. METHODS: Members of the Working Group further discussed the syntheses of the literature on definitions, theoretical underpinnings, operationalizations, and response shift methods. They outlined areas in need of further explication and refinement, and delineated additional implications for future research. RESULTS: First, the proposed response shift definition was further specified and its implications for the interpretation of results explicated in relation to former, published definitions. Second, the proposed theoretical model was further explained in relation to previous theoretical models and its implications for formulating research objectives highlighted. Third, ways to explore alternative explanations per response shift method and their implications for response shift detection and explanation were delineated. The implications of the diversity of the response shift methods for response shift research were presented. Fourth, the implications of the need to enhance the quality and reporting of the response shift studies for future research were sketched. CONCLUSION: With our work, we intend to contribute to a common language regarding response shift definitions, theory, and methods. By elucidating some of the major implications of earlier work, we hope to advance response shift research.
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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 | 0.571 | 0.809 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".