Knowledge, perceptions, and barriers influence public actions to help bees in Toronto, Canada
Bibliographic record
Abstract
Abstract Despite the enthusiasm surrounding bees, the public's current knowledge is sourced from the non‐native honey bee whose life history differs from many endemic North American species. Ascertaining the public's understandings and perceptions of bees is essential to implementing publicly supported conservation initiatives that may benefit bee conservation as well as social and ecological aspects of communities, especially in large cities which are epicenters of increasing urbanization. The knowledge and perception of bees as well as current actions and barriers to their conservation among Torontonians was assessed using an online survey. Participants held correct assumptions about basic bee biology pertaining to environmental importance and decline in cities but lacked awareness regarding species richness. Nonetheless, public support for bees was universally high. Individuals were mostly involved with low‐effort actions such as avoiding pesticide use and intense management but also reported planting wildflowers as well. Most participants indicated at least one barrier to action, with lack of knowledge, time, and money being most frequently reported. These barriers, including knowledge score and demographic characteristics such as lower age and lack of degree, influenced how many actions participants engaged in. Researchers should continue to create inclusive opportunities for public engagement while incorporating inter‐disciplinary approaches to mitigate current barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 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) | 0.008 | 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".