Softening‐Up Persistently Stubborn Institutional Stances in Net Zero Change Environments
Bibliographic record
Abstract
ABSTRACT The decarbonization of the built environment remains a strategically important net zero pathway. Relative to other net zero pathways, the built environment persistently lags behind. To address this problem, we use the theoretical lens of institutional stance to enrich the understanding of the cognitive‐cultural foundations of institutions, in particular how entrusted institutional members hold, reflect and assert specific stances towards decarbonization of the built environment. In this institutional stance analysis, we identify three institutional change practices of nudging, tugging and mooring for softening‐up persistently stubborn stances in the pursuit of net zero goals. The findings point to mooring mechanisms which permit institutional members to ‘step out’ via temporal institutional respites, and subsequently reconciling these stances through the practice of satisficing, technology cycling and contrasting. Finally, conclusions and practical implications are presented for softening‐up persistently stubborn stances in prolongated net zero change environments.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".