Strengthening the foundation of educational psychology by integrating construct validation into open science reform
Bibliographic record
Abstract
An increased focus on transparency and replication in science has stimulated reform in research practices and dissemination. As a result, the research culture is changing: the use of preregistration is on the rise, access to data and materials is increasing, and large-scale replication studies are more common. In this paper, I discuss two problems the methodological reform movement is now ready to tackle given the progress thus far and how educational psychology is particularly well suited to contribute. The first problem is that there is a lack of transparency and rigor in measurement development and use. The second problem is caused by the first; replication research is difficult and potentially futile as long as the first problem persists. I describe how to expand transparent practices into measure use and how construct validation can be implemented to bolster the validity of replication studies.
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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 | 0.556 | 0.699 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.009 | 0.085 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".