Painting the ‘Essential’ Green Activist: Critical Interrogations of Responses to Environmental Activism
Bibliographic record
Abstract
In this paper, qualitative Directed Content Analysis is used to elucidate and analyze media rhetoric and legal rhetoric found in prominent news publications reporting on green protests in Canada and the United States. Preliminary theory suggested commodification of the environment encourages the denigration of green dissenters and may aim to lead to negative conceptualizations of the ‘green activist’ in the public consciousness. Further content analysis notions that the political leanings of given news publications may be the strongest predictor of the level of support/opposition to green protest that an article will purvey. And, animal advocacy, youth-led dissent, protest around critical infrastructure, and disruptive-to-civilian protest are suggested to be solid predictors for heightened negativity in coverage. Furthermore, protestors were rarely depicted as threats to peoples’ safety but more often to the established status quo of the treadmill of production.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".