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.
Bibliographic record
Abstract
In a crowdsourcing contest, innovation is outsourced by a firm to an open crowd that competes in generating innovative solutions. Given that the projects typically consist of multiple attributes, how should the firm optimally design a crowdsourcing contest for such a project? We consider two alternative mechanisms. One is a joint contest, where the best solution is chosen from the joint solutions across attributes submitted by all contestants. The other is multiple separate parallel subcontests, with each dedicated to one attribute of the project. It is intuitive that the separate contest has the advantage of potentially creating a “cooperative” final solution contributed by different contestants. However, somewhat surprisingly, we show that the separate contest may reduce the incentive for the crowd to exert effort, resulting in the joint contest becoming the optimal scheme. The comparison of the expected best performances in the two contests depends on the project’s characteristics. For example, if contestants’ performances have a sufficiently high (respectively, low) level of randomness, the separate (respectively, joint) contest is optimal. If the number of contestants is large (respectively, small) enough, the separate (respectively, joint) contest is optimal. Moreover, we find that when the prize is endogenized, the optimal amount of the prize in the joint contest is no less than that in the separate contest. Finally, we extend the model to account for contestants with heterogeneous types. This paper was accepted by Gad Allon, operations management.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it