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Record W3020920749 · doi:10.1111/cdep.12364

Underused Methods in Developmental Science to Inform Policy and Practice

2020· article· en· W3020920749 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChild Development Perspectives · 2020
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsDevelopmental SciencePsychologyLife spanScale (ratio)Data scienceResearch designQuality (philosophy)Management scienceEngineering ethicsComputer scienceApplied psychologyDevelopmental psychologyGerontologyMedicineSociologySocial scienceEngineering

Abstract

fetched live from OpenAlex

Abstract In this article, we offer recommendations for underused innovations and advances in research methods to enhance the quality of developmental science research. We couch our recommendations in terms of measurement innovations, design innovations, and analytic advances. We discuss six methods—the visual analog scale, the retrospective pretest–posttest design, appropriate timing and units of change, the accelerated longitudinal design, missing data treatments, and integrative data analysis. We conclude by encouraging developmental scientists to continue to incorporate advances and innovations in methods as they address the essential questions surrounding our goal to understand and improve the human condition across the life span.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.304
GPT teacher head0.475
Teacher spread0.171 · 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