Perception of mentoring among emerging hunters: quantitative study focusing on Quebec
Bibliographic record
Abstract
Like other North American regions, Quebec has been affected for several years by a decline in the number of recreational hunters, which has had various economic, ecological and social impacts on hunting areas. Mentoring is one of the actions often taken to remedy this situation. Based on an online survey of 503 experienced and novice Quebec hunters, this study aims to analyse the perception of mentoring among up-and-coming hunters, and to estimate certain actions that could facilitate the recruitment of new hunters. We also discovered two distinct hunter profiles – Social recreation vs. Serious leisure – based on hunting habits and motivation. The results show that the perception of mentoring is largely positive, and that the relationships forged between mentor and mentee are positive, beneficial and useful. Nonetheless, there are nuances, particularly in relation to the experience and gender of the practitioner, leading to a fine-tuning of mentoring actions. Moreover, beginner and experienced hunters are well represented among both social and serious hunters, and we find that serious hunters, in particular, are prime candidates for transmitting mentoring initiatives.
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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 | 0.001 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".