Virtual(ly) Overnight: Providing Resources and Services in the era of COVID-19
Bibliographic record
Abstract
In response to the COVID-19 pandemic, on Monday March 16th, 2020 at 4:00pm the Health Sciences Library at the Northern Ontario School Medicine (NOSM) closed their physical spaces until further notice - becoming an exclusively virtual library overnight. It was imperative for the library to provide continuity of services to users - many of them preparing for the spread of COVID-19 to the resource limited North. The library operated virtually in many aspects prior to COVID-19. It regularly supports NOSM members at the school’s two campuses, but also learners, faculty and staff spread over 843,853 square kilometers of Northern Ontario Canada. With the closure of the library spaces however, the library was required to suspend physically dependent programming and reconsider operations and service delivery with all staff working from home. This case study will outline library operations prior to COVID-19 that have supported this transformation; the imperative component of messaging continuity of services to library users; the transition to staff working remotely; and the implementation of new tools and guides to support users virtually during the era of COVID-19.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".