MétaCan
Menu
Back to cohort
Record W4248353577 · doi:10.47678/cjhe.v48i1.187971

Resource Centre or Experience Desk? The Spatiality of Services to Indigenous and International Students at Universities in Ontario, Canada

2018· article· en· W4248353577 on OpenAlexaffvenueabout
Jean Michel Montsion

Bibliographic record

VenueCanadian Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsYork University
Fundersnot available
KeywordsDeskIndigenousMainstreamContext (archaeology)Space (punctuation)SociologyPoliticsResource (disambiguation)Public relationsWork (physics)Service (business)Higher educationPolitical scienceLibrary scienceGeographyBusinessMarketingEngineeringEcology

Abstract

fetched live from OpenAlex

In recent years, Ontario universities have increasingly targeted Indigenous and international students for recruitment. Focusing on three southern Ontario universities, I examine how service delivery for these student groups is organized in space. In light of Henri Lefebvre’s work, I argue that the spatiality of the information hubs created to support them differs significantly, each being defined in the interactions between institutional assumptions about the student group, the social presence and activities hosted, and the lived experiences of the students utilizing these services. Whereas Indigenous student services are organized as a resource centre to create a separate space for Indigeneity on campuses, international student services take the form of an experience desk to emphasize rapid integration into the mainstream. Based on interviews with students and staff, I reflect on the differences between the two models to discuss the spatial politics of information hubs within the context of Ontario universities.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.444

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0210.013
Scholarly communication0.0100.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.027
GPT teacher head0.327
Teacher spread0.300 · 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 designQualitative
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

Citations5
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicHigher Education Practises and EngagementFrench-language works237,207