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Record W1973852987 · doi:10.1080/09652540701794379

Avoiding Potential Traps in Fair Trade Marketing: A Social Representation Perspective

2008· article· en· W1973852987 on OpenAlex
Luc K. Audebrand, Adrian Iacobus

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Strategic Marketing · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversité de MontréalHEC Montréal
Fundersnot available
KeywordsPerspective (graphical)BusinessMarketingRepresentation (politics)Industrial organizationComputer sciencePoliticsPolitical science

Abstract

fetched live from OpenAlex

Because fair trade (FT) is a new and complex phenomenon, it needs to be appropriated, through a process of symbolic coping, by the members of a community in order for their behaviours to be affected. This symbolic coping is done through, and can be understood via, the production of social representations (SRs). SRs arise out of the need to make familiar those objects and phenomena that are uncommon. Whenever a new phenomenon such as FT permeates a social group, it inevitably passes through a process of appropriation. This process is by no means neutral; on the contrary, it draws on the ensemble of images, ideas and connotations already present in a population. This article first provides a detailed analysis of the phases of the FT appropriation process based on Social Representations Theory (SRT). The article then assesses the potential traps or risks for the future of FT related to the circulation of information and the creation of social knowledge about FT among populations in the North.

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.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.267
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.290
Teacher spread0.239 · 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