Bats and Perception : Enhancing Human-Bat Interactions Through Bat-Related Facts
Bibliographic record
Abstract
Introduction A collaborative project between SEEDS and UBC students to address residential concerns regarding the discovery of a M. lucifugus roost on UBC’s Vancouver campus found that many people still harbour negative attitudes towards bats despite their tremendous benefit to humans and the surrounding ecosystem. Our study explored the effect of education on people’s perception of bats. Specifically, our team investigated whether the demystification of myths surrounding bats or presenting facts of bat-related benefits impact human perception of bats. Research Question How does demystification of myths surrounding bats and bat-related benefits impact human perception of bats? Methods We created a between-subject survey to test the effectiveness of three different infographics, differing only in the type of fact presented (bat-neutral, bat-benefit, and bat-demystifying), on the participant's perception of bats. Our hypotheses were threefold: 1) participants exposed to demystification facts about bats will have a more positive perception of bats than those exposed to neutral bat-related facts, 2) participants exposed to demystification facts about bats will have a more positive perception of bats than those exposed to beneficial facts about bats, and 3) participants exposed to beneficial facts about bats will have a more positive perception of bats than those exposed to neutral bat-related facts. Results Results show that participant’s perception of bats did not significantly differ between the three conditions, failing to support our hypotheses. Recommendations Firstly, future studies should aim to recruit people in direct contact with bats such as those living or working near Auditorium Annex Offices located in UBC’s Vancouver campus. Additionally, future surveys should add a section asking what the participant’s preconceived beliefs are regarding bats and whether the participants believe the educational information presented. Finally, future studies should utilize different educational methods to convey bat-related facts and explore how these varying mediums can affect the public's perception of bats. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".