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Record W4387876909 · doi:10.1080/10572317.2023.2270888

Hidden Public Services

2023· article· en· W4387876909 on OpenAlexaff
Charlotte Innerd, Scott Gillies, Debbie Chaves

Bibliographic record

VenueThe International Information & Library Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCatalogingService (business)Work (physics)MetadataKnowledge managementPublic relationsBusinessWorld Wide WebStaff managementComputer sciencePolitical scienceManagementMarketingEngineering

Abstract

fetched live from OpenAlex

Libraries organize themselves into various departments reflecting their operations and the functional role of individual staff members. Traditionally, most libraries have defined forward facing services as public service. This understanding has often excluded those staff working within a technical services department (acquisitions, cataloging, eresource management), web design, digital services from viewing themselves as providing a public service. The authors analyze library functions in eresource management, licensing, metadata, and accessibility services to underscore the importance of understanding the shared nature of library work such that all staff in the library are seen to be working to promote the connection between patrons and resources, and thus have a connection to each patron who uses the library. By analyzing functional roles and sharing knowledge, libraries can enhance their organizational alignment to support patrons more holistically by broadening the definition of public services. The article emphasizes the necessity of ongoing communication, understanding, and the adoption of a values-based approach toward organizational culture and functioning.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.464

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0030.003
Scholarly communication0.0100.010
Open science0.0020.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1390.034

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 head0.036
GPT teacher head0.307
Teacher spread0.271 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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