Reimagining academic conferences: Toward a federated model of conferencing
Bibliographic record
Abstract
What should the post-COVID conference look like? In our attempt to answer this question, we first describe the primary functions and affordances of conferences. Our frank appraisal reveals the breadth of reasons why academics attend conferences, and how conference attendance often blends personal and professional motivations. We also elaborate some of the shortcomings of in-person conferences, spanning personal, professional, and societal concerns. Recent alternative (virtual) formats for convening scholars provide means for alleviating some of these shortcomings, but do not seem entirely up to the task of providing a fully satisfactory solution to all that conferencing can be. Moreover, we extrapolate from prior history and ongoing trends to predict that technological solutionism to conferencing is likely to unleash both positive and negative dynamics, some of which will exacerbate current ills in our profession. We then sketch out a values-based approach that can serve as a basis for reimagining academic conferences. This vision promotes a federated model of conferencing, grounded in principles of inclusion, diversity, community, and environmental stewardship.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".