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Record W3097092794 · doi:10.5860/crl.81.7.1107

Effectiveness of Vernacular Library Orientation Videos in Comparison with the English Language Equivalent

2020· article· en· W3097092794 on OpenAlexafffundabout
Jennifer Congyan Zhao, Tara Mawhinney

Bibliographic record

VenueCollege & Research Libraries · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsMcGill University
FundersMcGill University
KeywordsEnthusiasmVernacularPerspective (graphical)NarrativePerceptionComputer scienceEnglish languageOrientation (vector space)MultimediaLinguisticsMathematics educationPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Vernacular language videos with narration in non-English languages have been used in North American academic libraries to engage and empower international and non-native English-speaking students. This study investigated the effectiveness of McGill University Library’s orientation videos from the perspective of these students, using mixed methods to outline student learning, affective responses, and views on audiovisual features used in the video. Compared to the English video, vernacular language videos are equally effective in delivering content and more adept at invoking student enthusiasm about the library. These students’ perceptions on video design and audiovisual features are useful for librarians who use videos to engage a linguistically diverse campus.

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)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.094
GPT teacher head0.418
Teacher spread0.324 · 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".

Quick stats

Citations2
Published2020
Admission routes3
Has abstractyes

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