Shortcuts and Dead Ends: Control Issues With Online User-Generated Content
Bibliographic record
Abstract
Increasingly, internet users are creating and sharing content through a variety of socially-based websites. This includes sharing images, videos, stories, diary-like text, and personal information with others. With all of this information being created, what happens to user content once it has been uploaded to a site and effectively removed from the user's hands? We set out to explore this question within some of the popular user content hosting sites on the Internet. In doing so, we discovered a flawed paradigm wherein sites offer little guarantees as to service, but limit the precautionary measures that users can take themselves.De plus en plus, les internautes créent et partagent du contenu sur des sites web sociaux, notamment des images, des vidéos, des histoires, des entrées de journaux « intime » et des renseignements personnels. Par contre, qu'arrive-t-il lorsque le contenu de l'utilisateur est placé sur un site, hors du contrôle du créateur? Nous avons décidé d'explorer cette question sur certains sites sociaux populaires. Les résultats démontrent un paradigme imparfait où les sites offrent peu de garanties de service, mais limitent les mesures de précaution que peuvent prendre les utilisateurs.
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 | 0.031 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".