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Record W2606695229 · doi:10.33137/ijidi.v1i1.32183

Meeting Campus Linguistic Diversity: A Multilingual Library Orientation Approach

2016· article· en· W2606695229 on OpenAlexaffabout
Jennifer Congyan Zhao, Nazi Torabi, Sonia Smith

Bibliographic record

VenueThe International Journal of Information Diversity & Inclusion (IJIDI) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMcGill University
Fundersnot available
KeywordsMandarin ChineseSession (web analytics)AttendanceMedical educationDiversity (politics)Orientation (vector space)PsychologyPopulationLibrary scienceComputer scienceWorld Wide WebMedicineSociologyLinguisticsPolitical science

Abstract

fetched live from OpenAlex

This study sought to determine whether offering multilingual orientation sessions to non-native English-speaking students at the beginning of an academic year would improve their knowledge of library services and resources. In September 2015, McGill Library offered 11 orientation sessions in five different languages—English, French, Mandarin Chinese, Persian, and Spanish. A total of 74 students attended the sessions. Noticeable attendance patterns included: (1) sessions offered earlier in the semester had high attendance and (2) the Chinese sessions received the most participants. This study also evaluated students’ learning via an assessment questionnaire at the end of each session. The assessment results suggest an increase in students’ awareness of services and resources offered by McGill Library. This article reports on the planning, implementation, and assessment of this program; discusses the challenges encountered and lessons learned in organizing and delivering these sessions; and provides recommendations on organizing similar multilingual library orientation programs to address the needs of a diverse student population on 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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.023
Open science0.0020.005
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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2016
Admission routes2
Has abstractyes

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