Building community at distance: a datathon during COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it