Bibliographic record
Abstract
Academics have probably been organizing conferences since at least the time of Plato. More recently, academics have brought some of their conferences online. However, the adoption of online conferences is limited. One might wonder if scholars prefer traditional conferences for their ability to provide goods that online conferences cannot. While this may be true, online conferences outshine traditional conferences in various ways, and at a significantly lower cost. By considering the costs and benefits of both conference models, we may find reasons to prefer online to traditional conferences in some circumstances. This chapter shares the methods, quantitative results and qualitative results of the Minds Online conferences of 2015, 2016 and 2017. The evidence suggests that the online conference model can help scholars better understand their profession, share the workload of conference organizing, increase representation for underrepresented groups, increase accessibility to attendees, decrease monetary costs for everyone involved, sustain conference activity during states of emergency and reduce their carbon footprint. So, the advantages of traditional conferences might be outweighed by their higher costs after all.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.292 | 0.127 |
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".