There for the Reaping: The Ethics of Harvesting Online Data for Research Purposes
Bibliographic record
Abstract
Online social environments offer a rich source of data that researchers can harvest to gain insight into a wide range of social issues. This type of research is sometimes considered as observation of public behaviour, and therefore exempt from ethical review. This type of research, however, raises ethical issues with respect to the public/private nature of online spaces, consent, and anonymity in the online environment. This project examines research ethics guidelines for recommendations regarding the use of harvested online data, identifying best practices for researchers who engage in this type of research. Les media sociaux offrent une riche source de données que les chercheurs peuvent récolter pour mieux comprendre un large éventail de problèmes sociaux. Ce type de recherche est parfois considéré comme une observation du comportement du public, et donc exempt de tout examen éthique. Ce type de recherche, cependant, soulève des problèmes éthiques en ce qui concerne la nature publique / privée des espaces en ligne, le consentement et l'anonymat dans l'environnement en ligne. Ce projet examine les lignes directrices en matière d'éthique de la recherche pour des recommandations concernant l'utilisation des données récoltées en ligne, identifiant les meilleures pratiques pour les chercheurs qui s'engagent dans ce type de recherche.
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 | 0.240 | 0.350 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".