Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the manner in which advocates of crowdsourcing reconfigure the classical sociological treatment of the crowd. Design/methodology/approach The approach taken conceives of the semantics of crowd theorizing in three phases, each of which makes sense of the power dynamics between the elite and the crowd. In phases one and two, the crowd is conceptualized as a problem generator; in phase three, the crowd is depicted as a problem solver and innovator. Findings This paper provides a critical look at phase three crowd theorizing. It explores how, by ignoring the disruptive power dynamic, crowdsourcing generates a credible image of the crowd as an innovator and problem solver. The work concludes with a discussion of the implications of phase three crowd theorizing for researchers in sociology. Practical implications Advocates of the wisdom of crowds, if interested in the sociological implications of their position, must attend to both the disruptive and costly implications of third phase crowd theorizing. Originality/value This paper maps the crowdsourcing process and places it in context. It argues that the distance between the classical social scientific treatment of the crowd is not nearly as great as crowdsourcing advocates would have one believe. Nevertheless, phase three crowd theorizing opens up sociologically relevant questions regarding the future portrayal of collective intelligence as a form of virtual property.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".