Bibliographic record
Abstract
(English): This paper explores the potentials and perils of alternative social media, through a firsthand account of targeted harassment on a prominent decentralized social media network, Mastodon. It illustrates how both network architecture and norms place the onus on users for their own safety. Though singular in content, this case conforms to patterns for which minoritized users of the network have sought remedy for years. This matters because abusive behavior online is common and its burden falls heavily on women, racial, ethnic, gender and sexual minorities, and the like; the democratic potential of noncommercial, decentralized social media cannot be realized if enhancing accountability to users is not a priority. The paper argues for foregrounding accountability in the network, spanning sociotechnical relationships between and amongst users, moderators, and architects of the network. It suggests that relations of production and participation on decentralized social media be oriented towards “meshy accountability,” invoking both consciously woven connections and the gaps and spaces between them. (Spanish): Este artículo explora el potencial y los riesgos de las redes sociales alternativas a través de un relato directo de acoso selectivo en Mastodon, una prominente red social descentralizada. Ilustra cómo tanto la arquitectura como las normas de la red responsabilizan a los usuarios de su propia seguridad. Si bien su contenido es singular, este caso se ajusta a patrones que los usuarios minoritarios de la red han buscado solución durante años. Esto es importante porque el comportamiento abusivo en línea es común y su carga recae considerablemente sobre mujeres, minorías raciales, étnicas, de género y sexuales, entre otras. El potencial democrático de las redes sociales descentralizadas y no comerciales no se puede materializar si no se prioriza la rendición de cuentas a los usuarios. El artículo aboga por priorizar la rendición de cuentas en la red, abarcando las relaciones sociotécnicas entre usuarios, moderadores y arquitectos de la red. Sugiere que las relaciones de producción y participación en las redes sociales descentralizadas se orienten hacia una “rendición de cuentas mezquina,” invocando tanto las conexiones tejidas conscientemente como las brechas y espacios entre ellas.
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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 | 0.009 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.104 | 0.072 |
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".