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Record W4388715478 · doi:10.2426/aibstudi-13396

Conducting International Research in the Library and Information Science Field: Challenges and Approaches

2022· article· en· W4388715478 on OpenAlexaff
Kawanna Bright, Krystyna K. Matusiak, Debbie Schachter

Bibliographic record

VenueThe Scholarship East Carolina University's Institutional Repository (East Carolina University) · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Optimization
Canadian institutionsLangara College
Fundersnot available
KeywordsLibrary scienceComputer science

Abstract

fetched live from OpenAlex

International comparative research in the library and information science (LIS) field examines the processes and phenomena related to libraries and other information organizations and their users. with a focus on differences and similarities across countries or cultures. International research is challenging due to language barriers, ethical concerns, and the legacy of the colonial research model. This paper presents an international research project undertaken by members of the International Federation of Library Associations (IFLA) Library Theory and Research (LTR) Section which investigated the approaches to teaching research methods in LIS programs worldwide. The paper focuses on the project’s research design, on the research ethical issues and on the collection of multilingual data. It discusses the inherent challenges in conducting international research and outlines the approach to increasing the geographic and linguistic diversity of study respondents. The LTR research team adopted several strategies to recruit participants from multiple countries and collect data in three languages. The recruitment announcements were distributed throughout international and regional mailing lists in multiple languages. The survey instrument was translated from English to Spanish and French, and the interviews were conducted in English and Spanish. The authors also discuss the methodological advantages of mixed-methods design and the benefits and limitations of using surveys and interviews in international research.

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)0.460
metaresearch head score (Gemma)0.328
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.540
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4600.328
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0170.025
Science and technology studies0.0290.057
Scholarly communication0.0600.041
Open science0.0110.041
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.0060.002

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 head0.105
GPT teacher head0.250
Teacher spread0.146 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
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

Citations2
Published2022
Admission routes1
Has abstractyes

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