Values and attitudes toward canada geese (branta canadensis) and population management on the university of manitoba fort garry campus
Bibliographic record
Abstract
Canada geese populations have increased across North America and have now been declared overabundant in some jurisdictions. Human-geese conflicts may rise, making a case to better understand peoples’ views toward this highly adaptable species. This study’s goal was to gain a better understanding of a university campus community’s values and attitudes as well as acceptance of lethal management techniques toward Canada geese found on the campus. A self-administered online questionnaire, using a modified Tailored Design Method was applied to University of Manitoba students, staff and faculty. Findings showed a significant difference between campus users in both general wildlife and Canada geese value orientations. General wildlife value orientations predicted Canada geese value orientations, while Canada geese value orientations significantly predicted acceptance of lethal population management options. Using the Potential for Conflict Index 2 demonstrated that as the severity of conflict scenarios increased, the level of consensus about lethal management decreased and varied among users. All lethal management options were rejected in favour of public education. This study showed how human dimensions can help management authorities better understand interest groups that may have a relationship with wildlife species. In this case, it also confirmed support for increased public education to minimize human-geese conflicts on campus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".