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Record W104836670

Internationalization and the Canadian Academic Library: What Are We Offering?

2014· article· en· W104836670 on OpenAlexaboutno aff
Jeannie Bail, Ryan Lewis, Amanda Power

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsNoticeLibrary scienceCensusPopulationInternationalizationDeskInstitutionPolitical scienceHappeningHigher educationSociologyPublic relationsGeographyHistoryBusinessDemographyLaw
DOInot available

Abstract

fetched live from OpenAlex

Although the population of St. John’s, Newfoundland, is largely homogenous recent Census data reveal that its inhabitants are 98% Englishspeaking and visible minorities make up around 1% of the population one can’t help but notice that from behind the Information and Research
\nHelp desk at the Queen Elizabeth II Library (QEII) of Memorial University of Newfoundland (MUN), things look a lot more diverse than reflected in the general population (Statistics Canada, 2006, 2012). Despite the remoteness of the province and its small size (around half a million people call it home), what is happening on the MUN campus is part of a greater trend in academia affecting university campuses all over Canada and the United States, which is the rise of the international student. This research study was undertaken to examine the role of the library within the Canadian academic institutions these international students attend, and the services that are being offered to them. The main questions we sought to answer are: do academic libraries in Canada offer specialized services for international students? And, if so, what types of programs are being offered? This information will be useful for future program development at our institution, and other universities and colleges throughout Canada and the rest of the world.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0010.006
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.277
Teacher spread0.238 · 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
Published2014
Admission routes1
Has abstractyes

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