The Gendered Natures of Polar Bear Tourism
Bibliographic record
Abstract
This article offers a critique of nature-based Arctic tourism through a gender-aware analysis of representations associated with polar bear tourism in Churchill, Manitoba, Canada. The guiding purpose of our study was to analyze how "nature" is gendered in its construction and presentation through tourism, and to what effect. Our study focused on revealing dominant gendered expectations and understandings (re)produced in the Churchill polar bear tourism promotional landscape. Drawing on a critical discourse analysis of qualitative and visual promotional texts, we show how various representations of polar bear tourism impose hegemonic gender roles onto polar bear bodies, which are emplaced within a conventionally gendered landscape. As the "Polar Bear Capital of the World," Churchill's wildlife viewing industry relies on the (re)creation, dissemination, and maintenance of particular meanings and natures attributed to polar bears, as well as human–polar bear relationships, for economic benefit. This gives rise to questions about how power circulates with respect to Churchill's tourism production practices, gender being one of many axes of identity through which power operates and is interpolated. Ultimately, the article advances literature on gender-aware analyses of tourism and environment, and argues the promotion of gendered natures must be consistently questioned to create space for more equitable tourism practices.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".