Sentiment and attitudes toward offsetting and the biodiversity market in online media articles
Bibliographic record
Abstract
Biodiversity offsetting, a conservation approach to offset loss of habitat and ecosystem services, has been widely accepted and implemented in different legislative frameworks around the globe. I assigned sentiment scores (from -3 [very negative] to +3 [very positive]) to online news articles to examine public sentiment toward offsetting. I identified 86 pertinent articles published from 2013 to 2023 by web scraping online media outlets through keywords. I examined article content based on topics commonly associated in scientific literature with offsetting, such as risks or financial aspects. Most articles were from Australia (44%), 16% were from the United Kingdom, and 5% were from Colombia and Madagascar. Three distinct groups covered finances (n = 47), species, and social impacts of offsetting (n = 23) and offsetting frameworks (n = 16). Articles covering monetary and finance aspects had a lower predicted sentiment score (-0.72, 95% CI -0.98 to -0.47) than articles that covered new, alternative offsetting forms (-0.15, 95% CI -0.46 to 0.17), such as mitigation banking and credits. In articles focused on charismatic species and loss of livelihood, offsetting risk and social issues were associated with low sentiment scores (<-0.85). Sentiment scores were high for articles on offsetting at a multicountry or global scale (0.47, 95% CI -0.06 to 0.99), and scores were the lowest in Australia (-1.03, 95% CI -1.36 to -0.70). Public sentiment based on media articles was generally negative toward offsetting, and many of the ecological and methodological issues and risks were reflected in the articles, but mitigation measures as a prerequisite to offsetting were mentioned in only 18% of all articles. With the seemingly high public interest in conservation and hence offsetting, it will be imperative to expand the current breadth of information about offsetting that is being communicated or made available to the public.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".