Bibliographic record
Abstract
Human and ecological health are dependent on pollinators in myriad, interconnected ways, from providing food, fiber, and medicine, to supporting the very web of life that sustains living organisms on Earth. Pollinators are responsible for pollinating an estimated 35% of global crop volume and 90% of the flowering plants on Earth. While most global crop production happens outside of urban spaces, food production, ecosystem resilience, and urban health are inextricably linked through the services provided by pollinators. Despite good evidence of pollinator population declines due to human-induced causes such as habitat loss, pesticide use, and a changing global climate, there is very little research on the human dimensions of pollinator conservation, particularly in an urban context. Using a case study methodology, this research introduces the Bee City movement in Ontario, Canada, which is a conservation strategy that brings together municipal leadership with urban citizens. By embedding these efforts at the municipal level, the Bee City movement facilitates public and policy discourse through the primary criteria of habitat creation, education, and celebration. Addressing pollinator declines through municipal conservation efforts is an important intervention to ensure a healthy future for people, pollinators, and the planet. With a growing number of Bee Cities across North America, there is an intentional effort to foreground pollinator health in municipal planning. With active implementation, this has potentially far reaching tendrils from increasing interest and awareness to the creation of pollinator habitat on municipal, private, and residential property with all the associated benefits.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".