Issue Fields and Echo Chambers: Increasing field contestation fueled by moral emotions
Bibliographic record
Abstract
We investigate how issue fields with increasing levels of contestation can develop into fields characterized by echo chambers. Studying the introduction of a controversial new approach to addiction services—harm reduction—we explain how proponents’ and opponents’ rhetorical arguments changed over time, transitioning the issue field through different configurations. Our findings reveal how field actors were initially differentiated by moral convictions, and as their expression of moral emotions became more intense, the two groups became increasingly divided and polarized in their views, leading to an issue field characterized by echo chambers. Through our analysis of archival materials and interview data, we explicate this process by identifying three phases of issue field transition: creating a moral emotional divide; intensifying antagonization; and insulating against the other side. We contribute to the literature by presenting a model of change explaining how emotional rhetoric, together with different types of triggering events, can fuel increasing levels of contestation and drive the field toward developing echo chambers. Second, by taking a discursive view of issue fields with particular attention to rhetorical arguments, we provide foundational work for an institutional perspective on echo chamber—that echo chambers result from ongoing social processes where people encapsulate themselves based on a sense of right and wrong, in contrast to the predominant view of becoming trapped in an enclosed space. Third, through our focus on the role of moral emotions and how they can escalate in situations of contestation, we advance knowledge regarding the importance of emotions in field dynamics.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".