‘ANNE GOES ROGUE FOR ABORTION RIGHTS!’ EXPLORING DISCURSIVE MATERIALIZATION ACROSS AND BEYOND ONLINE PLATFORMS
Bibliographic record
Abstract
This presentation examines the social media campaign #SupportIslandWomen that was undertaken by reproductive rights activists in Prince Edward Island (PEI). The initiative gained popularity in 2016 due to both the off- and online circulation of posters throughout PEI landmarks depicting the Green Gables-like image of a young girl (“rogue Anne”) wearing red braids and a bandana. These posters showcased specific hashtags that encouraged debates on various online platforms. For this study, we underline how human actors invoked the symbolic ‘figure’ of rogue Anne to give weight to their own arguments by speaking or acting in her name. By ‘figure’, we mean any symbolic entity that is materialized through interaction and that possesses agency, or the ability to make a significant difference in interaction. Hence, our study examines the processes through which rogue Anne was made present in interaction, the role of digital (online) and physical (offline) affordances in the materialization of this figure, and the differentiated effects that these invocations generated. To do so, we build our dataset by performing non-participant observation on social media platforms and by exploring Canadian blogs and newspapers. Drawing from organizational discourse theory, our results show that invoking the figure of rogue Anne allowed for pro-choice collectives to assert their authority in abortion debates by labelling the fictional character as a modern feminist icon. They also underline the importance of studying the intervention of symbolic figures, their effects, and their materialization within political initiatives that incorporate and go beyond the practice of ‘hashtagging’.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".