Designing and deploying a ‘compact’ crowdsourcing infrastructure: A case study
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
The Web 2.0 phenomenon of ‘crowdsourcing’ is now an accepted means of enabling ‘democratic’ content creation and the validation and authorization of content online. However, the technical implementation of crowdsourcing systems is not without its challenges. Systems designed to accommodate extremely large crowds are easier to equip with techniques that exploit the signal to noise ratio to derive useful output. For smaller groups it is often less a matter of filtering out noise and more a matter of filtering out single voices clamouring to dominate discussion through barnstorming tactics or system circumvention. This article discusses and analyses a case study focused on the design and deployment of a ‘compact’ crowdsourcing infrastructure, a design specifically intended to subvert and overcome the shortcomings of applying well-proven large-scale collaborative methods to a recognizably smaller group.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 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