Building community at distance: a datathon during COVID-19
Bibliographic record
Abstract
Purpose This paper aims to use the experience of an in-person event that was forced to go virtual in the wake of COVID-19 as an entryway into a discussion on the broader implications around transitioning events online. It gives both practical recommendation to event organizers as well as broader reflections on the role of digital libraries during the COVID-19 pandemic and beyond. Design/methodology/approach The authors draw on their personal experiences with the datathon, as well as a comprehensive review of literature. The authors provide a candid assessment of what approaches worked and which ones did not. Findings A series of best practices are provided, including factors for assessing whether an event can be run online; the mixture of synchronous versus asynchronous content; and important technical questions around delivery. Focusing on a detailed case study of the shift of the physical team-building exercise, the authors note how cloud-based platforms were able to successfully assemble teams and jumpstart online collaboration. The existing decision to use cloud-based infrastructure facilitated the event’s transition as well. The authors use these examples to provide some broader insights on meaningful content delivery during the COVID-19 pandemic. Originality/value Moving an event online during a novel pandemic is part of a broader shift within the digital libraries’ community. This paper thus provides a useful professional resource for others exploring this shift, as well as those exploring new program delivery in the post-pandemic period (both due to an emphasis on climate reduction as well as reduced travel budgets in a potential period of financial austerity).
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".