Academia's Role in Climate Action: Enhancing Awareness and Reducing Environmental Impact of Academic Events
Bibliographic record
Abstract
A clean environment is vital for both population health and the well-being of our planet (Brusseau et al., 2019). In-person gatherings, like academic conferences, significantly contribute to carbon emissions that harm the environment, highlighting the importance of finding opportunities to reduce these emissions (Tao et al., 2021). Cognizant of the environmental cost of our annual national in-person research team gathering, our team sought to minimize the environmental impact of our 2023 gathering while raising awareness among team members. Our actions included using reusable tableware, composting food waste, and hosting an education session by the local environment office. Not only did our efforts increase awareness among gathering attendees about the importance of considering environmental impact reduction opportunities in their work, but they also catalyzed similar efforts at two national conferences. Our team took the lead in developing an evaluation of the environmental impact of each national conference and surveyed over 150 conference attendees. We collected demographic information, travel mode to the conference, and attitudes about environmental harms via a digital conference application. We also observed environmental actions implemented by event organizers and venue hosts. A carbon emission score and frequency distributions of attitudes surrounding the environment were calculated. Where possible, a food waste assessment was conducted. Findings showed that event organizers made efforts to minimize the environmental impact of their event by selecting a venue whose management was interested in collaborating; by encouraging attendees to carpool and use reusable water bottles; by serving plant-based meals, and by minimizing single-use products. Findings also revealed that most conference attendees consider it important to reduce the environmental impact of their professional lives. Given the value of face-to-face interaction in facilitating knowledge exchange (Chan et al., 2023), it is important that organizers continue to make efforts to reduce carbon emissions when planning events. References Brusseau, M. L., Ramirez-Andreotta, M., Pepper, I. L., & Maximillian, J. (2019). Environmental impacts on human health and well-being. In Environmental and pollution science (pp. 477-499). Academic Press. Chan, A., Cao, A., Kim, L., Gui, S., Ahuja, M., Kamhawy, R., Latchupatula, L. (2023). Comparison of perceived educational value of an in-person versus virtual medical conference. Canadian Medical Education Journal, 12(4), 65-69. https://doi.org/10.36834/cmej.71975 Tao, Y., Steckel, D., Klemeš, J.J. et al. (2021). Trend towards virtual and hybrid conferences may be an effective climate change mitigation strategy. Nature Communications, 12 (7324). https://doi.org/10.1038/s41467-021-27251-2
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".