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Record W7132908086

Usage patterns of NSW public libraries’ resources during the pandemic

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.<br/><br/>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 &amp; Collect to allow continued public access to physical resources.<br/><br/>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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0070.013
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0090.000

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