Catalysing the Irish Energy Transition: Capacities and Challenges
Bibliographic record
Abstract
The transition to a “low carbon, climate resilient and environmentally sustainable economy by the end of the year 2050” has been conceptualised as the “national transition objective” in the Irish Climate Action and Low Carbon Development Bill, passed in late 2015. This has raised a myriad of questions over how this can be operationalised and resourced and whether it can maintain political momentum. This paper assesses the utility of framings informed by the transitions (MLP) and technological innovation systems perspectives in contributing to transformative societal processes, by examining their application in an Irish case study on policy and technology. Through a qualitative exploration of the broader societal and policy context of the energy sector and a more detailed examination of the innovation systems of selected niche technologies (bioenergy and electric vehicles), the Irish case study sought to identify potential catalysts for a sustainability transition in the energy sector in Ireland: where these exist, how these are being built or enabled, and barriers to change. Following a discussion on the theoretical approaches used, a description will be given of how these were applied in the conducting of the research on transition in Ireland case study and the key findings which emerged. A critical reflection will then be made on the utility of these perspectives (as applied) to contribute to broader processes of societal transformation 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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".