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Record W2015785475 · doi:10.1108/00330330910954361

Building a virtual branch at Vancouver Public Library using Web 2.0 tools

2009· article· en· W2015785475 on OpenAlexaffabout
Kay Cahill

Bibliographic record

VenueProgram electronic library and information systems · 2009
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsVancouver Biotech (Canada)
Fundersnot available
KeywordsWorld Wide WebWeb 2.0OriginalityComputer scienceWeb standardsWeb developmentWeb serviceSociology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to demonstrate the work undertaken by Vancouver Public Library (VPL) in an effort to convert its website into a true virtual branch, both through the functionality of the website itself and by extending its web presence on to external social networking sites. Design/methodology/approach VPL worked with its development partners to conduct a thorough community consultation to ensure that the new VPL website would be truly user‐focused. Since the site's launch, VPL has made strategic management of both its internal and external web presence a key organisational priority, reflected in its creation of two new professional positions which include co‐ordination of VPL's internal and external web presence as part of their job specification. Findings VPL has demonstrated that it is possible to take a systematic, integrated, thoughtful approach to the adoption of Web 2.0 tools and technologies in order to enhance web services without sacrificing quality or control. Originality/value As many public libraries consider their options with regard to the integration of Web 2.0 tools and technologies, VPL offers an example of good practice in strategic selection and management of these tools to optimise the delivery of web‐based library services.

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)0.004
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: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.003
Scholarly communication0.0090.003
Open science0.0020.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0250.006

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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

Citations26
Published2009
Admission routes2
Has abstractyes

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