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Record W4296787972 · doi:10.18438/eblip30122

An Examination of Academic Library Privacy Policy Compliance with Professional Guidelines

2022· article· en· W4296787972 on OpenAlexvenueno aff
Greta Valentine, Kate Barron

Bibliographic record

VenueEvidence Based Library and Information Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsPrivacy policyTransparency (behavior)Privacy lawCodebookCoding (social sciences)Public relationsInformation privacyComputer scienceCompliance (psychology)Internet privacyPrivacy by DesignContent analysisPolitical scienceWorld Wide WebSociologyComputer securityPsychology

Abstract

fetched live from OpenAlex

Objective – The tension between upholding privacy as a professional value and the ubiquity of collecting patrons’ data to provide online services is now common in libraries. Privacy policies that explain how the library collects and uses patron records are one way libraries can provide transparency around this issue. This study examines 78 policies collected from the public websites of U.S. Association of Research Libraries’ (ARL) members and examines these policies for compliance with American Library Association (ALA) guidelines on privacy policy content. This overview can provide library policy makers with a sense of trends in the privacy policies of research-intensive academic libraries, and a sense of the gaps where current policies (and guidelines) may not adequately address current privacy concerns. Methods – Content analysis was applied to analyze all privacy policies. A deductive codebook based on ALA privacy policy guidelines was first used to code all policies. The authors used consensus coding to arrive at agreement about where codes were present. An inductive codebook was then developed to address themes present in the text that remained uncoded after initial deductive coding. Results – Deductive coding indicated low policy compliance with ALA guidelines. None of the 78 policies contained all 20 codes derived from the guidelines, and only 6% contained more than half. No individual policy contained more than 75% of the content recommended by ALA. Inductive coding revealed themes that expanded on the ALA guidelines or addressed emerging privacy concerns such as library-initiated data collection and sharing patron data with institutional partners. No single inductive code appeared in more than 63% of policies. Conclusion – Academic library privacy policies appear to be evolving to address emerging concerns such as library-initiated data collection, invisible data collection via vendor platforms, and data sharing with institutional partners. However, this study indicates that most libraries do not provide patrons with a policy that comprehensively addresses how patrons’ data are obtained, used, and shared by the library.

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 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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.284
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.373
Teacher spread0.312 · 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 teacher head, not a consensus.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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