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Record W6960797741 · doi:10.14288/hfjc.v16i1.828

Was it diffusion? Exploring the spread of daily physical activity policies in Canada

2023· article· en· W6960797741 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)CausationPolicy analysisShock (circulatory)Public policyConsistency (knowledge bases)Competition (biology)

Abstract

fetched live from OpenAlex

Background: Between 2005 and 2010, five Canadian provinces adopted daily physical activity (DPA) policies. This study investigated the adoption and spread of those DPA policies in the 5-year period. Purpose: The purpose of this study was to investigate the role, if any, of diffusion in the adoption and spread of DPA policies across provinces in Canada over a 5-year period. Methods: Semi-structured interviews were conducted with 15 DPA policy influencers. Transcripts were analyzed using directed content analysis to examine alignment with an established diffusion framework. Findings were also examined for consistency with mechanisms of policy diffusion and alternative explanations of policy spread. Results: Participant responses aligned most closely with diffusion framework components of attributes of the innovation, system antecedents for innovation, implementation and routinization, receptive context for change, assimilation by the system, system readiness for innovation, interorganizational networks and collaboration, and communication and influence. Findings also revealed evidence of policy learning, imitation, and competition across jurisdictions as the dominant mechanisms of policy diffusion. There was limited evidence that common shock and independent causation contributed to policy spread. Conclusions: The spread of DPA policies across Canada between 2005 and 2010 was consistent with theoretical concepts and mechanisms of policy diffusion.

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 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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.066
GPT teacher head0.258
Teacher spread0.193 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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