Opening Doors to Open Science and Scholarship for School Psychology Research, Training, and Practice
Bibliographic record
Abstract
Trustworthy scientific evidence is essential if school psychologists are to use evidence-based practices to solve the big problems students, teachers, and schools face. Open science practices promote transparency, accessibility, and robustness of research findings, which increases the trustworthiness of scientific claims. Simply, when researchers, trainers, and practitioners can ‘look under the hood’ of a study, (a) the researchers who conducted the study are likely to be more cautious, (b) reviewers are better able to engage the self-correcting mechanisms of science, and (c) readers have more reason to trust the research findings. We discuss questionable research practices that reduce the trustworthiness of evidence; specific open science practices; applications specific to researchers, trainers, and practitioners in school psychology; and next steps in moving the field toward openness and transparency.
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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 | 0.493 | 0.682 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.015 | 0.089 |
| Scholarly communication | 0.056 | 0.109 |
| Open science | 0.007 | 0.066 |
| Research integrity | 0.037 | 0.061 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".