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Not Virtual Enough: A Virtual Library’s Challenges During the COVID-19 Pandemic

2021· article· en· W3179239973 on OpenAlexaffvenueabout
Nicole Askin, Maureen Babb, Pamela W. Darling, Orvie Dingwall, Lenore Finlay, Kathy Finlayson, Cheryl Haas, Angela Osterreicher

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOutreachDoorsPandemicCoronavirus disease 2019 (COVID-19)Work (physics)Medical libraryLibrary scienceClosing (real estate)Public relationsBusinessPolitical scienceMedicineEngineeringComputer science

Abstract

fetched live from OpenAlex

As part of the University of Manitoba Libraries Outreach Services, the Winnipeg Regional Health Authority (WRHA) Virtual Library provides library services to hospitals, health centres, community health agencies, and personal care homes throughout the city of Winnipeg, Manitoba. All services of the WRHA Virtual Library, including the collection, are entirely virtual, though staff are physically located in the University’s health library. In March 2020, shortly after the World Health Organization declared the novel coronavirus disease (COVID-19) pandemic, libraries around the world started closing their doors and staff were required to work from home. The virtual infrastructure of our services and collections required no changes in how our patrons accessed the Virtual Library and a smooth transition was expected, but the sudden shift to working from home revealed gaps. This article discusses the unique experience of the WRHA Virtual Library transitioning to a completely virtual environment, the previous reliance on the University’s physical infrastructure, and the inequities identified between librarians and library technicians.

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)0.014
metaresearch head score (Gemma)0.026
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 score0.981
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0390.011
Scholarly communication0.0200.015
Open science0.0030.019
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.173
GPT teacher head0.391
Teacher spread0.218 · 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

Citations5
Published2021
Admission routes3
Has abstractyes

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