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Record W1489514068 · doi:10.18438/b8ks33

Library Catalogue Users Are Influenced by Trends in Web Searching

2006· article· en· W1489514068 on OpenAlexaffvenue
Susan Haigh

Bibliographic record

VenueEvidence Based Library and Information Practice · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsLibrary and Archives Canada
Fundersnot available
KeywordsSession (web analytics)Computer scienceWorld Wide WebTask (project management)VocabularySubject (documents)Library catalogThink aloud protocolThe InternetTest (biology)Information retrievalPsychologyUsability

Abstract

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A review of: Novotny, Eric. “I Don’t Think I Click: A Protocol Analysis Study of Use of a Library Online Catalog in the Internet Age.” College & Research Libraries, 65.6 (Nov. 2004): 525-37. Objective – To explore how Web-savvy users think about and search an online catalogue. Design – Protocol analysis study. Setting – Academic library (Pennsylvania State University Libraries). Subjects – Eighteen users (17 students, 1 faculty member) of an online public access catalog, divided into two groups of nine first-time and nine experienced users. Method – The study team developed five tasks that represented a range of activities commonly performed by library users, such as searching for a specific item, identifying a library location, and requesting a copy. Seventeen students and one faculty member, divided evenly between novice and experienced searchers, were recruited to “think aloud” through the performance of the tasks. Data were gathered through audio recordings, screen capture software, and investigator notes. The time taken for each task was recorded, and investigators rated task completion as “successful,” “partially successful,” “fail,” or “search aborted.” After the searching session, participants were interviewed to clarify their actions and provide further commentary on the catalogue search. Main results – Participants in both test groups were relatively unsophisticated subject searchers. They made minimal use of Boolean operators, and tended not to repair failed searches by rethinking the search vocabulary and using synonyms. Participants did not have a strong understanding of library catalogue contents or structure and showed little curiosity in developing an understanding of how to utilize the catalogue. Novice users were impatient both in choosing search options and in evaluating their search results. They assumed search results were sorted by relevance, and thus would not typically browse past the initial screen. They quickly followed links, fearlessly tried different searches and options, and rapidly abandoned false trails. Experienced users were more effective and efficient searchers than novice users. They used more specific keyword terms and were more persistent to review their search options and results. Through their prior experience, they knew how to interpret call numbers, branch library location codes, and library terminology such as ‘periodicals’. Participants expected the catalogue to rank results based on relevancy like an Internet search engine. While most were observed to understand intuitively the concept of broadening or narrowing a search, a ‘significant minority’ added a term to an already too-narrow search to improve their search results. When interviewed, participants suggested several ways to improve the catalog search query, such as adding summaries and contents, ranking results by relevance and degree of exact match to search terms, including an Amazon-like “find more like this” feature, and providing context-sensitive and interactive online help, especially at the point when a search has produced too many or too few hits. Conclusions – The study concluded that library catalogue users are heavily influenced by trends in Web searching. No matter what type of search a task called for, the participants tended to expect a simple keyword search to lead to optimal results presented in relevancy-ranked order. Because users do not generally know or care about the structure of a bibliographic record, and many have little concept of what a library catalogue is for or what it contains, Novotny suggests that user instruction needs to address these basics. He also suggests that library professionals and library system vendors must work together to address the clear evidence that library catalogues are failing their users.

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)0.003
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.279
Teacher spread0.267 · 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 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
Published2006
Admission routes2
Has abstractyes

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