The Wisdom of Consumer Crowds
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".