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Record W7132908086

Usage patterns of NSW public libraries’ resources during the pandemic

2022· report· en· W7132908086 on OpenAlexaff
Hamid R.; id_orcid 0000-0003-1232-6473 Jamali Mahmuei, Philip; id_orcid 0000-0001-6015-4958 Hider

Bibliographic record

VenueCharles Sturt University Research Output (CRO) · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsFuture Earth
Fundersnot available
KeywordsPublic accessPandemicProcess (computing)Public useCoronavirus disease 2019 (COVID-19)Loan
DOInot available

Abstract

fetched live from OpenAlex

In 2021 and 2022 the State Library partnered with Charles Sturt University on a project to understand the usage patterns of NSW public library resources during the COVID-19 pandemic. Previous research on the effects of the lockdown and library closures demonstrated the importance of public libraries to the community with library users indicating that access to collections was the most valued service, both before and during the pandemic. Throughout the COVID-19 pandemic public libraries put enormous effort into maintaining access to collections by expanding access to electronic material and introducing alternative creative solutions such as Click & Collect to allow continued public access to physical resources. The project team analysed detailed loan data from two public libraries to understand usage patterns. The project report Usage patterns of NSW public libraries’ resources during the pandemic outlines the research process and findings. The report describes changes in use of resources over time and usage pattern by type, genre and subject.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.178
GPT teacher head0.327
Teacher spread0.150 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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