Organizational (issue) Field Perspective on Climate Change
Bibliographic record
Abstract
We are in crisis mode. Climate change is simultaneously the grandest global challenge and a daily challenge to individuals' perceptions, motivations, and actions. Economic sociology equips us to examine the heart of this crisis: the means, institutions, and regulations of production, exchange, and consumption. To complement this, we must have theoretical and methodological approaches that simultaneously bridge these macro-global and micro-actor levels. The aim of our article is to propose a research agenda for examining climate change from a field perspective to serve as this bridge. Institutional theory defines the "field" as a unit of analysis, rather than focusing on solo organizations or people, to examine all relevant players in processes of stability and change. This concept is influenced by Bourdieu's (1977) notion of "social field" or socially constructed arena: how organizations' interests and activities are mutually constituted through the interactions between them. In this article, we answer three questions regarding the theoretical, methodological, and empirical benefits of taking a field perspective. Why is this helpful for examining climate change? We start with a brief discussion of the relevance of organizations for influencing CO2 production and for contributing to discussions on climate change. We then discuss the relevance of examining relational interactions, between organizations, in stabilizing or changing current positions towards debated actions and towards daily production practices. How is this approach usefully different? We propose that by combining two types of fields - organizational fields and issue fields - we can examine the relationships between organizational actions and discourse. From this we can examine what organizations are doing, how they are "talking," and why they are influenced by this. How does this provide actionable insights? Finally, we demonstrate how both types of fields can be captured simultaneously via big data approaches - by accessing the websites of thousands of organizations and by extracting how they link to each other. Such a research approach helps to inform our understanding of climate change debates and practices, highlights barriers, and offers alternative solutions.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".