Feminist Activists On‐line: A Study of the PAR‐L Research Network*
Bibliographic record
Abstract
Dans cet article, les auteures analysent une des premières listes de discussion électronique féministes canadiennes, PAR‐L (Liste politique, action, recherche). En se fondant en grande partie sur les résultats d'un sondage en ligne effectué auprès des abonnées et abonnés de PAR‐L, elles démontrent que, même chez les féministes sensibles aux questions de diversité et d'egalité, les écarts de pouvoir liés au sexe, à l'âge, au langage et à l'affiliation professionnelle influent sur l'interaction en ligne. Alors qu'elles reconnaissent que la présence simultanée de différences dans un espace partagé crée des tensions, elles soutiennent que cette caractéristique a également le potentiel d'améliorer la communication démocratique et d'approfondir la compréhension mutuelle à l'intérieur du mouvement des femmes, menant à un plus grand sens de la connexité et du bien‐etre chez les participantes. This paper presents an analysis of one of Canada's first feminist electronic discussion lists, PAR‐L (Policy, Action, Research List). Based largely on the results of an on‐line survey of PAR‐L subscribers, we show that, even among feminists sensitive to issues of diversity and equality, power differentials related to gender, age, language and professional affiliation influence on‐line interaction. While we acknowledge that the co‐presence of differences in a shared space creates tensions, we argue that it also has the potential to enhance democratic communication and deepen mutual understanding within the women's movement, leading to a greater sense of connectedness and well‐being among participants.
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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".