Making and Meeting Online: A White Paper on E-Conferences, Workshops, and Other Experiments in Low-Carbon Research Exchange
Bibliographic record
Abstract
Academics fly a lot: to research sites and archives, to conferences and workshops. Yet flying has many negative repercussions. Air travel has disproportionate climate impacts, and for reasons of time, money, and border security, produces many barriers for marginalized scholars, shaping who is able shows up at conferences and thus, who participates in the conversations that define a community of study. Forms of knowledge exchange that do not depend on aviation are thus urgently needed. E-conferences offer on such possibility. As scholars of media and energy, and as e-conference organizers and participants ourselves, we wrote this white paper to highlight what's worked in the past, what hazards lie ahead for the future, and what potential gains could be won in the present. We hope our words will be useful to small conference organizers and professional associations alike. Our aim is not to end in-person meetings but rather to foster effective low-carbon alternatives that can help reduce the amount of travel necessary to participate in global knowledge communities. Meeting together in person is invaluable, but we can augment it with effective alternatives through critical reflection and smart design choices. We aim to spark further reflections and innovations in collaborative experiments in digital research exchange - or even other forms of scholarly community. We hope such experimentation continues long after the pandemic is over, and that its effects will shape the university for the better.
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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 | 0.057 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".