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Record W4410480757 · doi:10.47989/ir30colis51916

Singing in the rain: the role of umbrella concepts in library and information science

2025· article· en· W4410480757 on OpenAlex
Ian Ruthven, Alison Hicks, Pamela J. McKenzie, Jenny Bronstein, Jette Seiden Hyldegård, Gunilla Widén

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInformation Research an international electronic journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsWestern University
Fundersnot available
KeywordsSingingComputer scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

Introduction. This paper considers the function and use of umbrella concepts within the library and information science (LIS) discipline. Method. This paper uses the example of information avoidance to examine how umbrella concepts shape LIS theoretical work, including how they impact the theorisation of an emerging discipline. Analysis. We use on Hirsch and Levin’s (1999) umbrella concept lifecycle to examine how umbrella concepts develop and, potentially, how they disappear. Results. We suggest that while umbrella concepts provide a useful way to unite disparate or emerging strands of research, they can also constrain the development of a field when the label becomes a convenience rather than an invitation to continue the theoretical work needed to progress scholarly constructs. Conclusions. We finish by considering how this examination of umbrella concepts plays into continued debates about the theoretical structure of LIS (or lack of it) as well as offering suggestions for future research priorities in this area.

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.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.133
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.380
Teacher spread0.367 · 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