Hard-line, unbending and assuming a lot: Institutional stance knotting responses towards energy transitions
Bibliographic record
Abstract
Advice abounds on how to successfully navigate through sustainable energy transitions using large-scale technology-fix solutions. Yet, historical evidence suggests that institutional problems are rarely, if ever, (re)solved entirely in this way (Oelschlaeger, 1979). Moreover, the looming planetary emergency poses significant systemic and peculiar challenges for managers and government policymakers alike. The conundrum of the climate emergency amplifies an age-old deficiency; that new emergent technology poses questions of institutional roles, agency and responses. The inflated ‘white knight myth’ of the technology-fix-solution as a primary transformative change agent capable of resolving the looming climate emergency, still holds sway over managers and policymakers tasked with searching for ‘quick win’ solutions. This paper illustrates how conceptualizing energy transitions as driven by one-best technological solution, or polar evaluative views of ‘good or bad’, is often counter-productive in terms of achieving complex change and long-term sustainability. Extant academic studies argue for a more nuanced and varied range of positions or stances in relation to how energy transitions can be achieved. Building on this initial work, we advance the argument that tackling the looming planetary emergency requires a stronger emphasis on understanding how to navigate institutional environments, taking into account the various stances which entrusted institutional members bring to the table when approaching sustainable energy transitions. We argue that paying close attention to the institutional stances enacted by various institutional members, we can generate insights into the navigation of the institutional environment. Our findings suggest that entrusted institutional members may attempt to cognitively ‘knot’ their stances in order to navigate hard-line stances through firstly partnership formation and engagement activities and secondly, through the bridging of existing metrics. In particular, we observe that the organisation of temporary sectoral clusters was found to facilitate the creation of social licence and enabled the management of perceptions of geothermal energy market transitions. Finally, we conclude by suggesting that institutional stance knotting can act as a way to temporarily reconcile hard-line stances when institutional members of the geothermal community faced tensions in the enactment of this energy transition.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".