The problem with alliances for the anti-fracking movement on the island of Ireland
Bibliographic record
Abstract
This research looks at the role of alliances in the context of one of the largest collective\nstruggles across the island of Ireland today, the anti-fracking movement. It presents the\nchallenges facing communities directly opposing this new emerging industry and challenges\nthem to critically reflect on their engagement with outside actors as they organise a collective\nopposition to it. This thesis explores how a green neo liberal hegemony controls the current\n‘environmental movement’ driven by powerful elites; multinationals, the state and state\nactors, and official ‘environmentalism’. With these one-time allies now largely absent,\ncombined with the growing threat of globalisation which ‘synergises’ power at the top to\nwork against social movements, has this left the anti- fracking movement fighting the\nfracking battle alone?\nDrawing from the experiences ofanti- fracking campaigners from the North West, Belfast and\nDublin, and outside activists from Ireland and Alberta, Canada, this research seeks to explore\nhow building a broad ranging alliance at the grassroots of these very actors can produce the\nmost effective resistance to corporate power. In an effort to contribute to activist knowledge,\nthis thesis aims to inform two main audiences; grassroots activists involved in the antifracking\nstruggle and ‘professional’ environmentalists. In an attempt to make the findings as\ninclusive as possible and for it to be applicable to both audiences it was difficult to contain\nthe word count of this thesis to twenty thousand words or indeed thirty thousand words!
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".