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Record W2342568536 · doi:10.1177/0276146715592929

Appropriation of Community Knowledge

2015· article· en· W2342568536 on OpenAlexafffund
Stefanie Beninger, June Francis

Bibliographic record

VenueJournal of Macromarketing · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsAppropriationHarmPublic relationsSubsistence agricultureBusinessKnowledge baseSociologyMarketingPolitical scienceLaw

Abstract

fetched live from OpenAlex

In Base of the Pyramid (BoP) and subsistence marketplaces literature, a general consensus prevails that the process of creating solutions for the poor is most successful when marketers gain a local perspective. This paper highlights that, as companies seek this local perspective within impoverished communities, they can appropriate community knowledge. Drawing on research in the area of community knowledge, an area of growing importance that is all but missing from the marketing literature, this paper explicates key features of community knowledge. Appropriation of community knowledge can have potential benefits to communities, but also can cause social harm, including undermining financial, economic, and cultural safety, in the BoP community. The papers proposes a framework, bridging ethical and legal approaches, that guides marketers to consider consent, cognitive justice, capacity, and community impact in order to mitigate harm and generate social benefits.

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)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0060.020
Scholarly communication0.0080.011
Open science0.0020.016
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.061
GPT teacher head0.269
Teacher spread0.208 · 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.

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

Citations16
Published2015
Admission routes2
Has abstractyes

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