EMERALD EXTRACTIVISM: BORDERS, ENERGY, AND DATA INFRASTRUCTURES IN IRELAND
Bibliographic record
Abstract
In November 2020, a video surfaced on Twitter showing the earth moving underneath the feet of a local hillwalker. The video documented a massive peat landslide at the border between Ireland and Northern Ireland, caused by the construction of the Meenbog Wind Farm. The landslide destroyed a swathe of active peat bog and polluted a watershed which spanned both sides of the border, prompting governmental and legal action from agencies and organizations in Ireland, Northern Ireland, and UK. A key piece of the puzzle, however, was that the Meenbog Wind Farm had in 2019 sold its future energy to global logistics and cloud giant Amazon to power its data centre operations in Dublin, over 200km away from this wind farm site in rural Donegal. The data infrastructure company’s decarbonization efforts were following fault lines and toxic legacies of colonial expansion, the imagined perpetual growth of data systems having unintended consequences at Ireland's contested internal border. By analyzing data centre and energy policy, public discourse around these infrastructural systems, and drawing upon site-specific fieldwork, this paper will confront the re-organization of political and environmental relations at the border with regards to emerging renewable energy and data entanglements. Engaging with vibrant discourses of “green extractivism” during the transition to renewable energy, the paper will approach bordering mechanisms cutting through Ireland as sites of contestation about what present and future extractive energy and data supply chains will look like, who will bear their burdens, and who will have a voice in shaping them.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| 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".