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Record W2936291752 · doi:10.1177/0829573519843027

Registered Reports, Replication, and the <i>Canadian Journal of School Psychology</i> : Improving the Evidence in Evidence-Based School Psychology

2019· article· en· W2936291752 on OpenAlexafffundabout
Steven R. Shaw, Joseph D'Intino, Ekaterina Lysenko

Bibliographic record

VenueCanadian Journal of School Psychology · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec-Société et Culture
KeywordsCredibilityPsychologyReplication (statistics)School psychologyTransparency (behavior)Evidence-based practiceProtocol (science)Foundation (evidence)Applied psychologyMedical educationClinical psychologyAlternative medicineMedicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

The Canadian Journal of School Psychology (CJSP) is offering scholars the opportunity to register research reports and make research protocols publicly available to promote replication, transparency, credibility, and utility for clinical practice. The purpose of this article is to outline the challenges regarding replication, reproducibility, and evidence-based practices, as well as describe the submission protocol and criteria for acceptance of registered reports. Advances and criticisms of the registered reports model are discussed. Although CJSP will accept submissions through the traditional peer-review model, registered reports and support of replication studies have the objective of promoting high-quality research to improve the research foundation for evidence-based practices in the profession of school psychology.

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

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.909
metaresearch head score (Gemma)0.969
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.981
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.9090.969
Meta-epidemiology (narrow)0.0040.010
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.0430.047
Science and technology studies0.0190.044
Scholarly communication0.0530.030
Open science0.0190.030
Research integrity0.0300.026
Insufficient payload (model declined to judge)0.0120.005

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.

Opus teacher head0.663
GPT teacher head0.518
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReproducibility
GenreCommentary

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".

Quick stats

Citations12
Published2019
Admission routes3
Has abstractyes

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