Heated Atmosphere: Organizational Emotions and Field Structuring in Online Climate Change Debates
Bibliographic record
Abstract
We use an organizational issue field to conceptualize how organizations partake in the signification of amorphous, yet intransigent issues – such as climate change. Following a network conception, field structure is captured as the linkages between organizations. Besides meaning system, and values, emotional expressions can affect the positioning of organizations within the field and, hence, how they define, debate, and address the core issue. Thus, we ask: How do framings, values, and emotion affect the structure of an organizational issue field? We answer our research question by synthesizing theories of fields, structuring, and emotion. By completing a network analysis of the online climate change debate, we find that organizations are most likely to link to other organizations expressing similar emotions – resembling organizational emotional entrainment. Expressed emotionality influences organizations’ positions in an organizational issue field even beyond cognitive framings like issue stance, values like moral worldviews, and typical in-group clusterings like organizational type or political orientation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".