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Record W1627996971 · doi:10.1111/spol.12152

Women's Organizations, Social Learning, and the Federal State: A Case Study of <scp>C</scp>anadian Pension Policy

2015· article· en· W1627996971 on OpenAlex
Christopher A. Cooper

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Policy and Administration · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPolicy learningFederalismState (computer science)Social policyWork (physics)Public administrationExploratory analysisPensionPolitical sciencePeriod (music)Public relationsExploratory researchPublic policyEconomicsSociologyLawSocial science

Abstract

fetched live from OpenAlex

Abstract The various ways which federalism influences gender policies has recently received a surge of academic interest. This article contributes to this literature by moving beyond formally adopted policies to study the influence of federalism on social learning amongst women's organizations. Using a most‐likely case study design, this exploratory work traces the policy positions held by women's organizations in C anada during a seven‐year period now known as the G reat P ension D ebate. Focusing on four empirical indicators of issue attention, participation in policy discussions, specificity of policy proposals, and consensus for reform, the findings suggest that the plurality and temporal proximity of successive policy venues – such as royal commissions and parliamentary committees – created by various governments offered women's organizations an optimum environment to engage in ongoing exchanges leading to the development, and greater specification, of policy positions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
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 head0.035
GPT teacher head0.355
Teacher spread0.320 · 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