TWITTERING RESEARCH, CALLING OUT AND CANCELING CULTURES: A STORY ANDSOME QUESTIONS
Bibliographic record
Abstract
This is an analysis of how Twitter played a significant, agentive and accountable role in the difficult birth and premature unravelling of a government-funded international feminist research network. We situate this case study and this process of initiation and annihilation within the broader context within which social media platforms are a critical site of intensive affective discursive practices through which individual and institutional reputations can be made and unmade, frequently with far reaching professional and or personal consequences. To date there has been little academic study of both the broader implications of the potential benefits of social media for academic networking and the perils. Two data sets are analyzed comparatively, the first, detailed written responses from an in-person workshop designed explicitly to gather feedback on the research network; the second a set of tweets that erupted over a number of days shortly after that event. Content analysis of both data sets shows the impact of a small, localized Twitter event on an international network of researchers, demonstrating the speed and thoroughness with which decades of research and collaboration can be undone, and raising larger questions about the sustainability of culturally precarious trajectories of work -- in this case feminist work -- within Twitter’s increasingly hegemonic media ecology.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.020 | 0.039 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".