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Record W7074034358

Relaunching a national social marketing campaign: expectations and challenges for the "new" ParticipACTION

2011· article· en· W7074034358 on OpenAlexaboutno aff

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMandateGeneral partnershipSocial marketingCompetition (biology)Influencer marketingMarketing researchIdeal (ethics)Public Sector Marketing
DOInot available

Abstract

fetched live from OpenAlex

ParticipACTION is a Canadian physical activity communications and social marketing organization that has been relaunched in 2007 after a 6-year hiatus. The purpose of this study is to qualitatively identify and describe the expectations and challenges the relaunch of the new ParticipACTION may present for existing physical activity organizations. Using a purposeful sampling strategy, the authors conduct semistructured telephone interviews with 49 key informants representing a range of national, provincial, and local organizations with a mandate to promote physical activity. Overall, there is strong support in seeing ParticipACTION relaunched. However, organizational expectations and/or their ideal vision for it are mixed. Organizations envision and support its performing an overarching social marketing and advocacy role, and in providing tools and resources that supplement existing organizational activities. Four major organizational challenges are identified concerning overlapping mandates, partnership and leadership concerns, competition for funding, and capacity concerns. Social marketing initiatives, such as ParticipACTION, may not be able to maximize their impact unless they address the expectations and concerns of competing organizations with a mandate to promote physical activity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.602
GPT teacher head0.300
Teacher spread0.302 · 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.

Study designTheoretical or conceptual
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

Citations4
Published2011
Admission routes1
Has abstractyes

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