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Record W3201646923 · doi:10.18438/eblip29955

Academic Libraries Report Minimal Standardization and Oversight of LibGuide Content

2021· article· en· W3201646923 on OpenAlexvenueaboutno aff
Sarah Schroeder

Bibliographic record

VenueEvidence Based Library and Information Practice · 2021
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary scienceStandardizationPsychologyComputer scienceInstitutionMedical educationSpace (punctuation)World Wide WebPublic relationsSociologyMedicinePolitical scienceSocial science

Abstract

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A Review of: Logan, J., & Spence, M. (2021). Content strategy in LibGuides: An exploratory study. The Journal of Academic Librarianship, 47(1), Article 102282. https://doi.org/10.1016/j.acalib.2020.102282 Abstract Objective – To determine what strategies academic libraries use to govern creation and maintenance of their LibGuides. Design – Online survey questionnaire. Setting – A selection of academic libraries that use Springshare’s LibGuide system, mainly in the United States and Canada. Subjects – Academic libraries with administrator level access to LibGuides at 120 large and small, private and public schools. Methods – Researchers made their online questionnaire available on a Springshare lounge and recruited participants through electronic mailing lists. Respondents were self-selected participants. The survey consisted of 35 questions, including several about their institution’s size and type, the number of LibGuides available through their library, and how their guides are created and reviewed. There was space available for comments. The survey stated that the researchers’ goal is to complete an “environmental scan of content strategies” in LibGuides at academic institutions. Main Results – Of the 120 responding institutions, 88% are located in either the United States or Canada and 53% reported that they do have content guidelines for LibGuide authors. Content guidelines might include parameters for topics, target audiences, or purpose. Parameters for structural elements, including page design, content reuse policies, naming conventions, and navigation, were most commonly represented at those institutions that reported having guidelines. Seventy-seven percent of respondents reported that their LibGuides do not go through a formal review process prior to publication. Regarding LibGuide maintenance, 58% reported that LibGuides are reviewed as needed, while 27% indicated a more systematic approach. In most cases, the LibGuide reviewer is the author, though sometimes a LibGuide administrator may take on a review role. The most common considerations for LibGuide review are currency, accuracy, usage, and consistency. Of the responding institutions, 74% reported that they do not conduct any user testing of their guides. Two of the biggest barriers to introducing and maintaining LibGuide guidelines identified in the survey were lack of time and a sense of librarian ownership over content and workflow. The strong culture of academic freedom may make some librarians resistant to following institutional guidelines. Survey respondents noted that, where content guidelines are present, they tend to address “low hanging fruit” issues, such as page design and naming conventions, rather than more complex issues around tone and messaging. Conclusion – Content creators tend to have many competing priorities, so a workflow and guideline system might help librarians spend less time on their guides. Despite a large amount of research on LibGuide best practices regarding content strategy, few institutions seem to be taking systematic steps to implement them. Further research examining the experiences of LibGuide authors and administrators and on the effectiveness of content strategy practices is necessary.

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)0.092
metaresearch head score (Gemma)0.294
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.485

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.294
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0140.019
Science and technology studies0.0070.009
Scholarly communication0.0190.015
Open science0.0070.013
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.020

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 head0.022
GPT teacher head0.249
Teacher spread0.226 · 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.

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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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