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Record W3210738660 · doi:10.7202/1076757ar

Post-Secondary Education in the Inner-City: Breaking barriers and building bridges in a divided city

2021· article· en· W3210738660 on OpenAlexaffabout
Shauna MacKinnon

Bibliographic record

VenueInternational Journal for Talent Development and Creativity · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsInner cityNeighbourhood (mathematics)CommissionSociologyAction (physics)Political scienceMedia studiesPublic administrationLawSocioeconomics

Abstract

fetched live from OpenAlex

The Department of Urban and Inner-City Studies (UICS) is a department in the faculty of Arts at the University of Winnipeg in Manitoba, Canada. The Department is located outside of the main campus in one of Canada’s poorest neighbourhoods. UICS is intentionally located here to offer access to postsecondary education to people who might not otherwise attend university. Our department aims to encourage people who have come to believe that university is ‘not for them’. It also serves to bring students from other areas of the city into the neighbourhood to begin to dispel long held misconceptions about the North End. We continue to develop our critical, place-based model in the spirit of putting ‘reconciliation into action’. As described by Senator Murray Sinclair, the former Chair of the Truth and Reconciliation Commission of Canada, it is “‘up to society’ to step up and take the actions that are needed." (CBC 2017). At UICS, we are committed to ‘stepping up’ by creating opportunities for learning through honest dialogue, and challenging systemic divides in our community.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.370

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 studies0.0000.000
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 head0.039
GPT teacher head0.376
Teacher spread0.337 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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