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Record W2092665781 · doi:10.1177/1524500414553737

Fostering Equity Through Downstream, Midstream and Upstream Social Marketing

2014· article· en· W2092665781 on OpenAlexaffabout
François Lagarde

Bibliographic record

VenueSocial Marketing Quarterly · 2014
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsLucie and André Chagnon Foundation
Fundersnot available
KeywordsMidstreamSocial marketingPublic relationsDownstream (manufacturing)Upstream (networking)PovertyGeneral partnershipPromotion (chess)Context (archaeology)Political scienceEconomic growthBusinessMarketingEngineeringEconomicsPolitics

Abstract

fetched live from OpenAlex

The mission of the Lucie and André Chagnon Foundation, Canada’s largest private foundation, is to prevent poverty by contributing to the educational success of young Quebecers. The Foundation embraces a comprehensive approach. It supports parents, community mobilization, and the emergence of a societal movement to advance early childhood development and student retention. The Foundation has also developed an innovative philanthropic partnership with the Quebec government. Social marketing principles and practices are systematically applied in some of its initiatives, such as the promotion of parenting behaviors. In light of the multiple factors associated with complex issues such as child development and equity, social marketers need to go beyond downstream behavior change programs in their quest to make a meaningful contribution. They must increasingly adapt their social marketing practice to the context of community participation (midstream) and policy (upstream) initiatives—wherever they are members of multidisciplinary and multistakeholder teams.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0080.006
Open science0.0010.011
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.094
GPT teacher head0.436
Teacher spread0.342 · 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 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

Citations5
Published2014
Admission routes2
Has abstractyes

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