MétaCan
Menu
Back to cohort
Record W2920077988 · doi:10.3138/cjpe.53070

How We Model Matters: A Manifesto for the Next Generation of Program Theorizing

2019· article· en· W2920077988 on OpenAlexaffvenue
Sebastian Lemire, Jane Whynot, Steve Montague

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsManifestoSociologyEpistemologyScale (ratio)Engineering ethicsComputer sciencePolitical scienceLawPhilosophyEngineering

Abstract

fetched live from OpenAlex

Abstract: In this concluding article, grounded on the exemplary contributions contained in the preceding pages, the guest editors scale the proverbial soapbox and present a manifesto to guide the pursuit and advancement of the next generation of program theorizing. Formulating ten declarations for program theory development and examination, the modest hope of the authors is to motivate and inspire refl ective evaluation practitioners to broaden their views, approaches, and techniques for future program theorizing.

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)0.088
metaresearch head score (Gemma)0.074
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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.074
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.048
Scholarly communication0.0200.024
Open science0.0020.008
Research integrity0.0040.015
Insufficient payload (model declined to judge)0.0070.001

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 head0.636
GPT teacher head0.522
Teacher spread0.114 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Program EvaluationSame topicEvaluation and Performance AssessmentFrench-language works237,207