#Participating #Contesting: Studying Counterpublics’ Discourses on Twitter About the Social Acceptability of Medical Assistance in Dying Legislation in Canada
Bibliographic record
Abstract
This article explores debates on medical assistance in dying (MAID) in Canada as they unfolded on Twitter before its adoption in June 2016. The opposition, which came from diverse groups-religious, experts, politicians-led to polarizing debates about the social acceptability of this measure. Our finding shows that the so-called lay citizens refused to leave the discussion to experts and politicians and got involved in the debates around the issue. Our results also show that Twitter was mainly used to share information, hence complementing the role of traditional media. Overall, the platform gave rise to an "ambient political participation," allowing minority or marginalized groups as well as lay citizens to share their knowledge and opinion about MAID. This may have favored a certain form of empowerment.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".