Mobilising a counterhegemonic idea: Empathy, evidence, and experience in the campaign for a Supervised Drug Injecting Facility (SIF) in Dublin, Ireland
Bibliographic record
Abstract
Abstract Using the case of the campaign to establish a Supervised Injecting Facility (SIF) for people who use illicit drugs in Dublin, Ireland, this paper makes three related contributions to contemporary literatures. First, by detailing the history of the campaign and paying particular attention to the ways it was influenced by learning from models elsewhere in the world, the paper adds a spatial perspective to research on the intersections of public health and politics. Second, the paper addresses the policy mobilities literature's minimal engagement with the role of counterhegemonic ideas and national states in shaping inter‐local policy circulations. It provides detailed empirical analysis of the influence of counterhegemonic ideas and how activists reference those ideas through appeals to empathy, expert evidence, and experience as they build coalitions to influence formal state institutions, including the legal system and the national government. This discussion supports a call for engagement between policy mobilities and counterhegemonic social movement literatures. Third, the paper addresses ongoing discussions of ‘failure’ in policy‐making by arguing for a critical, contextual approach to the spatialities and temporalities of attempts to change entrenched policy and regulatory models. The case study is based on one author's direct involvement in the campaign for a SIF and on semi‐structured research interviews with 12 key actors conducted since 2015. The research also involved an analysis of relevant documentary materials spanning the period 2012–2021 and both authors' participation in a drug policy forum in Dublin in January 2017, involving local and international actors.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.056 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| 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".