From toxic industries to green extractivism: rural environmental struggles, multinational corporations and Ireland’s postcolonial ecological regime
Bibliographic record
Abstract
This article articulates the historical entanglement of Ireland’s big tech ecosystem with earlier forms of economic development and state-sanctioned polluting practices, particularly focusing on strategies for attracting multinational companies since the 1960s. The outsourcing of polluting multinational industries to Ireland’s rural regions has a long history, one tied into fault-lines of the country’s postcolonial condition and developmental economy in the 1970s and 1980s. During this era of economic liberalisation, Ireland’s environmental politics were frequently organised against the outsourcing of toxic chemical, pharmaceutical, and technological industries to rural Ireland. Ireland’s position as a western European nation-state undoubtedly means that wealth accumulated via these industries merits complicity in the global supply chains sustaining “green” extractivism in the Global South. But rural Ireland also bears an uneven share of responsibility for these industries, whose destructive externalities are often imposed on these places through large-scale infrastructures. Historical struggles for environmental justice in these rural sites foreground access to land, livelihoods, health, and cultures of place. Contributing to recent debates on the colonial endurances of contemporary “green” development, we argue that these rural movements should be a starting point for a “just” transition attuned to anti-imperialist goals in Ireland.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".