Tangled Roots: Personal Networks and the Participation of Individuals in an Anti-environmentalism Countermovement
Bibliographic record
Abstract
We focus on the personal networks of members of an anti-environmentalism countermovement in a small town in Canada (Port Alberni, B.C.) that mobilized against the environmental movement. Drawing primarily from social survey data, we investigate the effects of network-based mobilization processes, and contending-movement ties (ties to the environmental movement), on level of participation in the countermovement. We add to the literature on networks and social movements, and movement-countermovement dynamics by 1) comparing network processes amongst a counter movement with those amongst a corresponding social movement, and 2) comparing personal network structures and mobilization processes between countermovement members and the general public. We find a similar pattern of network-based micromobilization processes amongst movement and countermovement participant networks. We find both similarities, and key differences between the counter movement and the general public in terms of activism and social network ties. Theoretical predictions have suggested that individuals who have ties to opposing groups will moderate their participation in a social movement. However, in this study of a community countermovement organization in a small town in Canada that mobilized against the provincial environmental movement we find that the number of contending movement ties (the range of ties to environmental organizations) held by individuals in the countermovement has a significant positive association with countermovement activism, and is the strongest statistical predictor of countermovement activism. Drawing upon both theory and substantive information we discuss the implications of this novel finding.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".