Bibliographic record
Abstract
In order for online communities to assemble and grow, some basic infrastructure is necessary that makes possible the aggregation of the collective action. There is a very intimate and complex relationship between the technological infrastructure and the social character of the community which uses it. Today, most infrastructure is provided by corporations and the contrast between community and corporate dynamics is becoming increasingly pronounced. But rather than address the issues, the corporations are actively obfuscating it. Wikiwashing refers to a strategy of corporate infrastructure providers where practices associated to their role of profit seeking corporations (such as abusive terms of use, privacy violation, censorship, and use of voluntary work for profit purposes, among others) that would be seen as unethical by the communities they enable are concealed by promoting a misleading image of themselves associated with the general values of wikis and Wikipedia (such as sharing and collaboration, openness and transparency). The empirical analysis is based on case studies (Facebook , Yahoo! and Google) and triangulation of several methods.
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 | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".