Registered Reports in School Psychology Research: Initial Experiences, Analyses, and Future
Bibliographic record
Abstract
This editorial is companion to the first registered report published in the Canadian Journal of School Psychology entitled, “Scope of School Psychological Practice in Germany: Part 1” by Alexa von Hagen et al. This manuscript outlines the importance of evidence-based practices in school psychology, identifies weaknesses in the foundation of current research practices, and discusses registered reports as a tool to enhance research rigor by mitigating biases such as p-hacking and publication bias. Registered reports have gained traction despite initial reservations from researchers due to perceived constraints and barriers; this approach to publication of scholarly articles can lead to a positive shift in the relationship between authors and editors during the publication process, fostering collaboration, transparency, and credibility in the research practice and ultimately leading to improved evidence-based practice. Initial experiences from the first registered report in the field of school psychology are examined, noting advantages, disadvantages, and future recommendations. The adoption of registered reports signifies a cultural shift toward more robust, transparent, and credible research practices in school psychology, leading to true evidence-based practice with increased likelihood of implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.437 | 0.728 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.040 | 0.029 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.014 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".