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Record W2149741875 · doi:10.1177/0276146708325382

The Wisdom of Consumer Crowds

2008· article· en· W2149741875 on OpenAlex

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 Macromarketing · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsYork University
Fundersnot available
KeywordsCrowdsGrassrootsConsumption (sociology)CreativityMarketingSociologyCollective intelligenceSharing economyPublic relationsBusinessKnowledge managementSocial sciencePolitical sciencePsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Past theories of consumer innovation and creativity were devised before the emergence of the profound collaborative possibilities of technology. With the diffusion of networking technologies, collective consumer innovation is taking on new forms that are transforming the nature of consumption and work and, with it, society and marketing. We theorize, examine, dimensionalize, and organize these forms and processes of online collective consumer innovation. Extending past theories of informationalism, we follow this macro-social paradigm shift into grassroots regions that have irrevocable impacts on business and society. Business and society need categories and procedures to guide their interactions with this powerful and growing phenomenon. We classify and describe four types of online creative consumer communities—Crowds, Hives, Mobs, and Swarms. Collective innovation is produced both as an aggregated byproduct of everyday information consumption and as a result of the efforts of talented and motivated groups of innovative e-tribes.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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