Getting to zero : a field-level perspective on organizational transitions towards carbon neutrality
Bibliographic record
Abstract
Climate change policies are proliferating at a local and regional level. Within this landscape, organizational climate change action is shifting from voluntary to mandated, and organizations are grappling with new pressures to reduce their environmental impact. This dissertation explores organizational responses to climate change policy, though a field-level analysis of 132 organizations that were required to achieve carbon neutrality in British Columbia, Canada. The strategies organizations adopted or considered over a five-year period from the policy inception are examined using survey data and a content analysis of annual reports. This study shows that the organizations bound by the carbon neutral mandate quickly came to a common understanding of what the practical expression of carbon neutrality involved. Within five years of the policy introduction, and three years of the requirement to become carbon neutral, organizations were considering or adopting a large number of similar strategies in response to the legislative requirement to reduce their carbon emissions. This convergence of strategies can be explained by several factors. First, organizations drew cues about appropriate responses from the government, and from other organizations within the field, leading to isomorphism of strategies over time. Second, the organizations were working under a common set of institutional logics, or cultural assumptions about the rationale for pursuing strategies, leading them to consider the same practices appropriate for meeting carbon neutral goals. Finally, organizations were supported by similar networks of organizations, centralizing the field around a few key actors. Similarity in responses to the mandate to achieve carbon neutrality are reflective of the fact that organizations drew from the common sources of information and resources to meet emissions reduction targets. This work demonstrates that organizational responses to climate policy should be understood with reference to the field in which organizations are embedded, rather than simply as the sum of individual organizational actions. It also highlights the fact that if the institutional and cultural conditions are right, organizational fields can rapidly emerge and adapt to new policy imperatives to tackle climate change.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.020 | 0.045 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".