Cashing in on Cetourism: A Critical Ecological Engagement with Dominant E‐NGO Discourses on Whaling, Cetacean Conservation, and Whale Watching<sup>1</sup>
Bibliographic record
Abstract
Abstract: This paper engages critically with the monolithic presentation of whale watching as the antithesis of whale hunting. It begins by tackling the reductive and homogenized portrayal of whale watching in mainstream environmental discourse as diametrically opposite to whale hunting and argues that such discourse likely obscures the existence of bad whale watching conduct. Next it reveals significant continuities between whale hunting and whale watching, especially the fetishized commoditization of cetaceans and the creation of a metabolic rift in human–cetacean relations. In both contexts nature is produced first and foremost according to capitalist principles, which problematizes the pervasive assumption that whale watching correlates primarily and directly with conservation. Finally, the paper examines two different business models and the production of distinct ecological and community development effects. The results of the comparison justify the need for more critical and effective environmental non‐governmental organization approaches to cetourism vis‐à‐vis nature conservation goals.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.049 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| 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".