New extractive frontiers in Ireland and the moebius strip of wind/data
Bibliographic record
Abstract
This article maps the interconnections between two emergent resource frontiers in Ireland: wind and data. Adding to literature about extraction and extractivism, we account for how these expanded extractive frontiers are mobilised within self-sustaining and automated formations. In Ireland, digital infrastructures such as data centres are developed by multinational tech companies to avail of a naturally cool climate and business environment friendly to their investment, part of a wider extractive system by which data are made valuable for their expansive operations. Wind farms similarly make use of Ireland’s climate to generate energy, often used to power digital infrastructures, and are increasingly embedded within ‘smart’ energy and data systems. Wind and data are seen discretely as ‘abundant’ resources, their infrastructures built on terra or (offshore) mare nullius, and their operations ‘green’. However, their infrastructures are entangled with non-renewable energy systems and tax evasive capital, and built across existing communities and environments through policy, planning logics and increasingly automated methods of maintenance and optimisation. Through what we call ‘the moebius strip of wind/data’, wind and data infrastructures are increasingly formidable in dictating our energy futures. In this article, we articulate how they are connected and how we can disentangle them, especially in their operation across urban and rural geographies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".