Vicariously wild: dwelling with wolves in conservation stories
Bibliographic record
Abstract
Into the wild I went. Searching for wolves, I discovered wolf-dogs and passionate people, dwelling together in centres designed for the tourists that the staff welcomed. The wolves/wolf-dogs were more wary… \n \nThe purpose of this session is to reflect on conservation stories by focusing on the way wolves are presented within them. Conservation stories are used in various ways – to encourage people to visit places, to help them connect with the animals and landscape, and to linger in memories (and photos) afterwards. The task of these stories is to illuminate, enhance and bring meaning to experiences. Conservation stories have a foundation in empirical science, yet embrace narrative, emotive methods to convey information in a manner that resonates with listeners and readers. These are more than just stories, they are peer-reviewed for robustness, with a strong evidence-base and aim to help ‘… bring conservation science to life’. I will share a story from my research exploring publicly-accessible conservation-education programmes in UK and Canada. I have utilised a fieldwork survey model, to capture my emotional response to my encounters, which I have explored through autoethnographical writing. I dwell alongside wolves in their stories as I reflect on the meaning of my experiences.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".