Automation, Algorithms, and Politics | Keeping Ottawa Honest—One Tweet at a Time? Politicians, Journalists, Wikipedians and Their Twitter Bots
Bibliographic record
Abstract
WikiEdits bots are a class of Twitter bot that announce edits made by Wikipedia users editing under government IP addresses, with the goal of making government editing activities more transparent. This article examines the characteristics and impact of transparency bots, bots that make visible the edits of institutionally affiliated individuals by reporting them on Twitter. We map WikiEdits bots and their relationships with other actors, analyzing the ways in which bot creators and journalists frame governments’ participation in Wikipedia. We find that, rather than providing a neutral representation of government activity on Wikipedia, WikiEdits bots and the attendant discourses of the journalists that reflect the work of such bots construct a partial vision of government contributions to Wikipedia as negative by default. This has an impact on the public discourse about governments’ role in the development of public information, a consequence that is distinct from the current discourses that characterize transparency bots.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".