Future of crowdsourcing and value creation in different media environments
Bibliographic record
Abstract
The focal theme of the panel is crowdsourcing and its future. Crowdsourcing is a relatively new concept, meaning broadly the act of outsourcing a job that is traditionally performed by e.g. an employee of a firm to an undefined, generally large group of people. Famous cases of crowdsourcing include Iron Sky the movie, crowdsourcing part of their fund raising and even parts of the actual movie making to movie fans; the intermediary firm InnoCentive offering the opportunity for other firms to crowdsource e.g. parts of their product development to crowds of people, and Canadian GoldCorp mining corporation crowdsourcing gold resource finding to both professionals and amateurs. Other crowdsourcing objectives include various tasks normally held within companies, such as marketing campaign design, product design, software testing, etc. The panel aims to provide fresh views for the opportunities of crowdsourcing from different angles, including various media environments and industry sectors, companies offering novel crowdsourcing services and platforms, as well as the viewpoint of value creation and business.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".