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Record W4382701654 · doi:10.4324/9781003180890-2

Is Canadian higher education under attack by neoliberal policies?

2023· book-chapter· en· W4382701654 on OpenAlexaboutno aff
Michael Kariwo

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

The role of higher education in any country is a contentious issue for the stakeholders who include politicians, parents, corporate organizations as well as students. In this chapter, I examine the values and purposes of higher education in Canada and the changes that have been taking place. I argue that in recent times Canadian higher education has been shifting from a liberal education to a more neoliberal one. Higher education in Canada is a provincial responsibility with the federal government having less direct control with the exception of a few colleges such as those providing military and naval training. While there is decentralization to provinces, there are broad similarities between institutions. The framework is sociological. I use an intersection of neoliberalism, and post-structuralism as a theoretical framework and basis of analysis. Neoliberalism has pillars in globalization as well as commodification of education. Post-structuralism is useful in capturing sites of resistance. There is evidence of forms of academic capitalism. The implications suggest more inequity and marginalization of new immigrants, refugees and those from low-income families including First Nations and Aboriginal people. It is critical that new models of higher education be developed in Canada so that the country can enhance its multicultural policy and ideology.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.982
Threshold uncertainty score0.922

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0180.015
Scholarly communication0.0150.005
Open science0.0020.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.055
GPT teacher head0.345
Teacher spread0.290 · 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.

Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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