MétaCan
Menu
Back to cohort
Record W2905047743 · doi:10.18438/eblip29478

Research Support Priorities of and Relationships among Librarians and Research Administrators: A Content Analysis of the Professional Literature

2018· article· en· W2905047743 on OpenAlexafffundvenue
Cara Bradley

Bibliographic record

VenueEvidence Based Library and Information Practice · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsUniversity of Regina
FundersCanadian Association of Research LibrariesAssociation of Research Libraries
KeywordsContent analysisThematic analysisLibrary scienceCoding (social sciences)SociologyPsychologyQualitative researchComputer scienceSocial science

Abstract

fetched live from OpenAlex

Abstract Objective - This research studied the recent literature of two professions, library and information studies (LIS) and research administration (RA), to map the priorities and concerns of each with regard to research support. Specifically, the research sought to answer these research questions: (1) What are the similarities and differences emerging from the LIS and RA literatures on research support? (2) How do librarians and research administrators understand and engage with each other’s activities through their professional literatures? (3) Do Whitchurch’s (2008a, 2008b, 2015) concepts of bounded-cross-boundary-unbounded professionals and theory of the “third space” provide a useful framework for understanding research support? Methods - The research method was a content analysis of journal articles on research-related topics published in select journals in the LIS (n = 195) and RA (n = 95) fields from 2012-2017. The titles and abstracts of articles to be included were reviewed to guide the creation of thematic coding categories. The coded articles were then analyzed to characterize and compare the topics and concerns addressed by the literature of each profession. Results - Only two (2.2%) RA articles referred to librarians and libraries in their exploration of research support topics, while six (3.1%) LIS articles referred to the research office or research administrators in a meaningful way. Of these six, two focused on undergraduate research programs, two on research data management, and two on scholarly communications. Thematic coding revealed five broad topics that appeared repeatedly in both bodies of literature: research funding, research impact, research methodologies, research infrastructure, and use of research. However, within these broad categories, the focus varied widely between the professions. There were also several topics that received considerable attention in the literature of one field without a major presence in that of the other, including research collaboration in the RA literature, and institutional repositories, research data management, citation analysis or bibliometrics, scholarly communication, and open access in the LIS literature. Conclusion - This content analysis of the LIS and RA literature provided insight into the priorities and concerns of each profession with respect to research support. It found that, even in instances where the professions engaged on the same broad topics, they largely focused on different aspects of issues. The literature of each profession demonstrated little awareness of the activities and concerns of the other. In Whitchurch’s (2008a) taxonomy, librarians and research administrators are largely working as “bounded” professionals, with occasional forays into “cross-boundary” activities (p. 377). There is not yet evidence of “unbounded” professionalism or a move to a “third space” of research support activity involving these professions (Whitchurch, 2015, p. 85). Librarians and research administrators will benefit from a better understanding of the current research support landscape and new modes of working, like the third space, that could prove transformative.

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)0.039
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.134
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0300.025
Science and technology studies0.0060.005
Scholarly communication0.0120.009
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.387
GPT teacher head0.481
Teacher spread0.094 · 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".

Quick stats

Citations7
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueEvidence Based Library and Information PracticeSame topicData Quality and ManagementFrench-language works237,207