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Record W4388833699 · doi:10.32674/jis.v15i1.5135

Canadian Universities in the Pandemic

2023· article· en· W4388833699 on OpenAlexaffabout
Dale Kirby

Bibliographic record

VenueJournal of International Students · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)RevenueWork (physics)Content analysisInclusion (mineral)Study abroadData collectionPolitical science2019-20 coronavirus outbreakHigher educationPublic relationsSociologyPsychologyEconomic growthPedagogyBusinessSocial scienceAccountingEconomicsMedicineInfectious disease (medical specialty)Engineering

Abstract

fetched live from OpenAlex

The COVID-19 pandemic had a significant impact on university enrollments around the world and caused significant challenges for students. This study examined how Canadian universities responded to the financial impacts of the pandemic on international and domestic students and how these responses differed. This work involved the identification and collection of public statements issued by selected Canadian universities during the first two years of the pandemic. This consisted of about 14,000 items posted on university public websites. Under the broad framework of the ecological theory of inclusion, an emergent theme content analysis was used to examine and organize data. This analysis confirmed that there was far less financial assistance provided to international students, and the amount of assistance provided to them was significantly disproportionate to the overall revenues generated by their enrollment at Canadian institutions.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.855

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0240.005
Scholarly communication0.0080.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.021
GPT teacher head0.377
Teacher spread0.355 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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