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Record W2794557027 · doi:10.1080/23322969.2018.1455532

Exploring the potential contribution of college bachelor degree programs in Ontario to reducing social inequality

2018· article· en· W2794557027 on OpenAlexaffabout
Michael L. Skolnik, Leesa Wheelahan, Gavin Moodie, Qin Liu, Edmund Adam, Diane Simpson

Bibliographic record

VenuePolicy Reviews in Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of TorontoInstitute for Christian Studies
Fundersnot available
KeywordsBachelorMandateEquity (law)LimitingInequalityPolitical scienceSocial mobilityBaccalaureate DegreeHigher educationHistorically black colleges and universitiesPublic administrationPublic relationsEconomic growthSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

During the past two decades community colleges and technical institutes in several jurisdictions, including parts of Canada, the United States and Australia, have been given the authority to award bachelor degrees. One of the motivations for this addition to the mandate of these institutions is to improve opportunities for bachelor degree attainment among groups that historically have been underserved by universities. This article addresses the equity implications of extending the authority to award baccalaureate degrees to an additional class of institutions in Canada’s largest province, Ontario. The article identifies the conditions that need to be met for reforms of this type to impact positively on social mobility and inequality, and it describes the kinds of data that are necessary to determine the extent to which those conditions are met. Based on interviews with students, faculty, and college leaders, it was found that regulatory restrictions on intra-college transfer from sub-baccalaureate to baccalaureate programs and lack of public awareness of a new type of bachelor degree may be limiting the social impact of this reform.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.414
GPT teacher head0.475
Teacher spread0.061 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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