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Record W4364356798 · doi:10.1139/bcb-2023-0021

Analysis of financial challenges faced by graduate students in Canada

2023· article· en· W4364356798 on OpenAlexaffvenueabout
Sarah Jane Laframboise, Thomas J. Bailey, Anh-Thu Dang, Mercedes Rose, Zier Zhou, Matthew D. Berg, Stephen Henry Holland, Sami Aftab Abdul, Kaela O’Connor, Sara El‐Sahli, Dominique Boucher, Garrett Fairman, Jacky M. Deng, Katherine Shaw, Nathaniel Noblett, Alexa D’Addario, Madelaine Empey, Keaton Sinclair

Bibliographic record

VenueBiochemistry and Cell Biology · 2023
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsCanadian Cardiovascular SocietyUniversity of Ottawa
Fundersnot available
KeywordsGraduate studentsDebtResearch councilStudent debtPolitical scienceGraduate educationFinancePublic relationsMedical educationGovernment (linguistics)BusinessMedicine

Abstract

fetched live from OpenAlex

Graduate students are vital to the creation of research and innovation in Canada. The National Graduate Student Finance Survey was launched in 2021 by the Ottawa Science Policy Network to investigate the financial realities of Canadian graduate students. Closing in April 2022, the survey received 1305 responses from graduate students representing various geographical locations, years of study, fields of education, and demographic backgrounds. The results capture a snapshot into graduate student finances, including an in-depth analysis of stipends, scholarships, debt, tuition, and living expenses. In its entirety, we found that the majority of graduate students are facing serious financial concerns. This is largely due to stagnant funding for students both from federal and provincial granting agencies and from within their institutions. This reality is even worse for international students, members of historically underrepresented communities, and those with dependents, all of whom experience additional challenges that impact their financial security. Based on our findings, we propose several recommendations to the Tri-Council agencies (Natural Sciences and Engineering Research Council, Social Science and Humanities Research Council, and Canadian Institute for Health Research) and academic institutions to strengthen graduate student finances and help sustain the future of research in Canada.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.179
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.088
GPT teacher head0.385
Teacher spread0.297 · 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.

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

Citations19
Published2023
Admission routes3
Has abstractyes

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