Report on Digital Literacy in Academic Meetings during the 2020 COVID-19 Lockdown
Bibliographic record
Abstract
COVID-19, a novel coronavirus, was deemed a pandemic during mid-March 2020. In response, lockdowns were imposed for an indefinite period world-wide. Academic institutions were no exception. Continuing meetings of academic groups consequently necessitated online communication. Various platforms were available from which to choose to encourage digital literacy. Despite alternatives, the almost overnight closure of all non-essential services at one post-secondary institution resulted in the selection of Zoom as the preferred platform for meetings until social distancing ended. In contrast, the facilitator of a unique, health-related, narrative research group at the institution—a group tailored to critical thought, communication, cooperation and creativity—considered a hybrid format private Facebook group likely to provide a more appropriate and satisfying group experience than possible with synchronous Zoom meetings. Pros and cons of both online platforms are presented along with the conditions under which each one is preferable. Positive results were evident in promoting digital literacy for this particular academic group using the hybrid format of a private Facebook group. As such, private Facebook groups hold promise in supporting digital literacy for collaborative online health-related group meetings. Unique in examining and evaluating private Facebook groups, this report holds significance for digital literacy regarding academic meetings.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".