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Record W4405540087 · doi:10.18438/eblip30554

Evidence Synthesis Instructional Offerings in Library and Information Science Programs

2024· article· en· W4405540087 on OpenAlexaffvenueabout
Meghan Lafferty, Zahra Premji, Philip Herold, Megan Kocher, Scott Marsalis

Bibliographic record

VenueEvidence Based Library and Information Practice · 2024
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceLibrary instructionLibrary scienceWorld Wide WebData scienceInformation retrievalInformation literacy

Abstract

fetched live from OpenAlex

Objective – The goal of this study was to determine the extent to which evidence synthesis (ES) is incorporated into American Library Association (ALA)-accredited master’s level Library and Information Studies (LIS) programs. The study considered the depth of coverage, interest in additional curriculum content, and preferences for expanding existing coverage. Methods – A cross-sectional survey was implemented. Program administrators and instructors currently involved with ALA-accredited master’s level LIS programs in Canada and the United States were eligible to participate. Recruitment emails targeted faculty and administrators from a directory of institutions offering ALA-accredited MLIS programs. Results – 26 eligible responses from 20 unique institutions were obtained. Most respondents reported that ES is incorporated into the curriculum, albeit only briefly in most cases. Most of the respondents expressed interest in incorporating more ES content into the curriculum, specifically as a portion of a course. A greater number of respondents would prefer to bring in external guest speakers to teach the ES content, but a small percentage were interested in training for existing LIS instructors. Conclusion – In-depth instruction on ES in LIS programs is currently limited. However, there appears to be interest in increasing ES content in curricula, primarily in the form of guest lecturers.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.542
Open science0.0000.000
Research integrity0.0000.000
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 head0.086
GPT teacher head0.360
Teacher spread0.273 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
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

Citations0
Published2024
Admission routes3
Has abstractyes

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