Urban Wildlife Management Planning Process and Conflict Mitigation: A Case Study of Denver’s Canada Goose Management Plan
Bibliographic record
Abstract
In 2019 and 2020, the USDA in coordination with Denver Parks and Recreation removed 2,174 geese from 6 different parks within the City of Denver. The removals and use of lethal methods to manage the concerns related to the geese population in Denver’s parks caused a public conflict and resulted in multiple legal challenges with the City of Denver. The opposition group claimed that the city did not sufficiently engage with the public in the formation of the goose management plan, and did not provide any public notification about the plan to remove geese. City officials have claimed that attempts to use non-lethal methods to manage the geese population in the parks have been unsuccessful, but the opposition group has claimed that the city has not used non-lethal management methods to the extent necessary to provide adequate results. An examination of the events that unfolded surrounding this conflict, and the public engagement processes and policies of Denver Parks and Recreation, identify opportunities to improve the public engagement methods of Denver Parks and Recreation. Improvements to record-keeping, accessibility to records, and improved accuracy of reports could enhance public engagement efforts. Denver Parks and Recreation could reduce public conflicts in the future through various public engagement process improvements. In addition, an examination of the range of non-lethal goose management methods that are available to be used, and an examination of methods that have been shown to be successful with other municipalities is presented to aid in the reduction of public conflicts regarding the management of geese in Denver’s parks in the future. Advisor: Zhenghong Tang
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".