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Record W2771441679 · doi:10.1177/0002764217744132

Do Subnational Governments Fund Organizations in Neoliberal Times? The Role of Critical Events in Provincial Funding of Women’s Organizations

2017· article· en· W2771441679 on OpenAlexaffabout
Emma Kay, Howard Ramos

Bibliographic record

VenueAmerican Behavioral Scientist · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAusterityPoliticsContext (archaeology)State (computer science)Civil societyPolitical sciencePublic administrationNova scotiaNeoliberalism (international relations)Economic growthEconomicsSociologyGeography

Abstract

fetched live from OpenAlex

Countries around the world have adopted neoliberal or austerity policies. Among states that fund organizations, this may have a detrimental effect on civil society. Looking at the Canadian context, this article examines whether subnational governments, provinces, step in during times of national budget cuts and changing political environments to fund organizations. We do this by analyzing the effect of critical events, regime changes, and the founding of key organizations on state funding in the province of Nova Scotia between 1960 and 2014. We do this to examine how the interaction of national and subnational political context shapes subnational funding of organizations. We find that critical events appear to be linked to increases in provincial funding, however, do not appear to be linked to cuts in funding. Regime changes and founding of key organizations have less clear-cut relationships with provincial funding.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.028
GPT teacher head0.371
Teacher spread0.344 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations8
Published2017
Admission routes2
Has abstractyes

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