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Record W2074476541 · doi:10.1080/15245000802034697

International Survey on Advanced-Level Social Marketing Training Events

2008· article· en· W2074476541 on OpenAlexaboutno aff
Sameer Deshpande, François Lagarde

Bibliographic record

VenueSocial Marketing Quarterly · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsSocial marketingPopularityMarketingVariety (cybernetics)Medical educationTraining (meteorology)SustainabilitySocial mediaPsychologySample (material)Public relationsBusinessMedicinePolitical scienceComputer scienceGeographySocial psychology

Abstract

fetched live from OpenAlex

The rising popularity of social marketing as a framework for social change has resulted in an increased demand for advanced-level social marketing training. As a result, an online survey was conducted in early 2007 to identify the social marketing training needs of social sector professionals. A convenient sample of 477 respondents from 33 countries (but primarily from the United States and Canada) responded to the online survey. Respondents expressed an interest in learning a variety of topics. “Audience analysis” was ranked the highest followed by “sustainability of change.” Benefits from and barriers to attending training events were identified. The primary motivation of the respondents to attend a training event was to apply concepts directly to initiatives on which they are currently working. The preferred format of training and other such details were also investigated. Findings from this survey should help trainers and institutions that offer face-to-face training events better respond to advanced-level training needs in the field of social marketing.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.007

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 head0.072
GPT teacher head0.276
Teacher spread0.204 · 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 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

Citations4
Published2008
Admission routes1
Has abstractyes

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