MétaCan
Menu
Back to cohort

Canadian university research libraries during the early days of the COVID-19 pandemic

2021· article· en· W3168611661 on OpenAlexaffvenueabout
Channarong Intahchomphoo, Michelle Brown

Bibliographic record

VenueCanadian Journal of Information and Library Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsUniversity of OttawaLibrary and Archives Canada
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Closing (real estate)Coping (psychology)Library science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political scienceScale (ratio)Public relationsCollection developmentBusinessPsychologyComputer scienceMedicineGeographyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This paper documents the services and changes that library staff at a group of 15 Canadian university research libraries highlighted on their main pandemic pages and social media accounts during the early days of the coronavirus (COVID-19) pandemic. Findings suggest that libraries in the samples adopted the following services and changes: closing the physical libraries; suspending all physical collection services and in-person events; continuing to provide virtual reference services; promoting access and usage of electronic collections; suspending late fees and renewing checked-out items with a new due date; and advising users to wait before returning borrowed items. Notably, all libraries in this study are operating as full full-scale as digital libraries. This study will provide lessons learned to other libraries around the world to help in reviewing their own operational policies for coping with the current COVID-19 pandemic and for future global public health crises.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.761

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.010
Science and technology studies0.0280.007
Scholarly communication0.0160.005
Open science0.0030.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.046
GPT teacher head0.275
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Information and Library ScienceSame topicLibrary Science and AdministrationFrench-language works237,207