Understanding anti-environmentalism : content analyzing the blogosphere for insight into opposition to environmentalism
Bibliographic record
Abstract
Environmentalism, like any other noteworthy social movement, has been met with some resistance. Opposition to this movement has come both from the general public and from organized anti-environmental groups. The closeness, or lack thereof, between the organized groups' messages and those of the public has yet to be clearly defined. Given that organized groups are often more capable of getting their message out to a larger audience, it is important to know to what extent the thoughts and ideas they put forward are representative of those of the public. Without examining this relationship, responding to anti-environmental sentiment in the public will be difficult.In an effort to understand opposition towards environmentalism in the general public, this project examined the blogosphere. Anti-environmental weblog (blog) postings were subjected to a content analysis in order to reveal common themes present within them. The specific focus of the analysis was on the manner in which environmentalism was portrayed by its opponents, as opposed to points of factual disagreement. Comparisons were then made to the arguments of the organized anti-environmentalism factions, and a more complete picture of the opposition toward environmentalism was constructed. From this basis, recommendations for a response to anti-environmental sentiment from leaders in the area of sustainable development were given.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".