Demystifying the Eastern Massasauga Rattlesnake
Bibliographic record
Abstract
The Eastern Massasauga rattlesnake (Sistrurus catenatus), Ontario’s only extant venomous snake, has suffered from the overall negative image of snakes created by centuries of traditional storytelling and more recently, mainstream media. Snakes in such media have often been portrayed as evil and dangerous creatures. This paper will examine whether non-fictional visual storytelling can be and should be used as a way of dismantling the misconceptions people might have of rattlesnakes, particularly the Eastern Massasauga rattlesnake (EMR). The Toronto Zoo conducts annual EMR workshops, a traditional form of environmental education, in hopes of trying to dispel the myths, stories and legends that have surrounded these rattlesnakes for decades. Alongside Toronto Zoo’s Adopt-A-Pond wetland conservation programme, we produced a short non-fictional educational film on the EMR and are now incorporating this film into the workshops in hopes to further the conservation messaging. This is the case study for this paper, as my research is concerned with the urgency of integrating visual storytelling to benefit wildlife conservation messaging, especially focusing on the Eastern Massasauga rattlesnake found in Ontario, Canada. This paper will contribute to our understanding of popular media depictions of snakes, as well as add to the existing literature about Disney animated films influence on our imaginations. This paper explores how the portrayal of snakes has been shaped by popular mass media, particularly through present-day Disney animated films and how conservation-based communities should respond in reducing such misconceptions that have risen in our modern societies, especially in children, over the years. My research will conclude by emphasizing on how art & digital media should be used as an educational tool for conservation concerns, causes and organizations such as the Toronto Zoo’s Adopt-A-Pond wetland conservation programme.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".