Vast Perceptions and Ambivalent Attitudes: The Cultural Construction Of The “Raccoon Capital” Of The World
Bibliographic record
Abstract
Encounters between humans and raccoons are increasing in frequency as both population densities rise. These encounters spur a vast range of individual perceptions and attitudes concerning raccoons. Moreover, human perceptions and attitudes toward other animals intersect with conspecific relationships. Therefore, this study aims to illuminate individual and collective social perceptions and attitudes through the exploration of discourse data collected over a tenyear duration from Toronto Wildlife Centre (TWC), the only wildlife rescue and rehabilitation centre in Ontario. Following a mixed-methods exploration of the data using NVivo, results reveal that the language used to describe human-raccoon encounters may be rooted in either of two competing social constructs that vary across individuals: an ethic of compassion for other animals or a social construction of risk that perpetuates stereotypes. Subsequently, further research aimed towards exposing implicit stereotypes is integral to deconstruct the problematic notions that mutually reinforce denigration when oppressions interlock.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".