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Record W2989082820

Effect Sizes for Models of Longitudinal Data (SESP 2019)

2019· article· en· W2989082820 on OpenAlexaboutno aff
Elizabeth Page‐Gould

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2019
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEconometricsMathematics
DOInot available

Abstract

fetched live from OpenAlex

Page-Gould, E., Sharples, A. E., & Song, S. (2019, October). Effect sizes for models of longitudinal data. In P. Shrout (Chair), Modeling Mediation Processes in Longitudinal Data. Symposium conducted at the annual meeting of the Society of Experimental Social Psychology, Toronto, ON, Canada.

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)0.062
metaresearch head score (Gemma)0.210
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.938
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.210
Meta-epidemiology (narrow)0.0040.007
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0080.008
Science and technology studies0.0010.003
Scholarly communication0.0050.007
Open science0.0080.008
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.1080.033

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 head0.101
GPT teacher head0.376
Teacher spread0.274 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractyes

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