"Everyone Loves Marineland!" (?): Entertainment Animal Advocacy, Praxis, and Resisting Corporate Repression
Bibliographic record
Abstract
Following allegations and graphic evidence of animal cruelty and neglect documented by ex-employee whistleblowers of Marineland Canada to the Toronto Star newspaper in late 2012, the ethics surrounding animal captivity have been increasingly contested in regional public discourse. Animal advocates in the Niagara region and beyond have been compelled to demand change at the infamous local captive animal park— whether it be welfare-oriented reform, or radical animal liberation. With this as a backdrop, this research explores the ideologies, experiences, and strategic tactics of anti-Marineland animal advocates; the sociopolitical issues surrounding the largely unexamined but serious issue of imprisoned animals as entertainers; and the ensuing governmental and corporatist attempts to squash dissent of anti-Marineland critics. Situated within a Critical Animal Studies theoretical paradigm as well as a flourishing global anti-captivity critique inspired by the film Blackfish, this project employs semi-structured interviews and participant observation methodologies to analyze advocates' views on captivity under capitalism and the effectiveness of their praxes. Finally, this research illuminates the nuances of the conventionally-upheld dualistic theoretical debate of animal welfare versus animal rights within zoo and aquaria entertainment contexts through an exploratory examination of advocates' complex ideological views.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".