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Record W4413608009 · doi:10.20355/jcie29723

Neoliberal White Corporate Saviourism in Public Education Outsourcing: A Critical Examination of Project 11

2025· article· en· W4413608009 on OpenAlexaffvenueabout
Christine Mayor, Melanie D. Janzen, Hafizat Sanni-Anibire

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOutsourcingWhite (mutation)BusinessPolitical scienceChemistryMarketing

Abstract

fetched live from OpenAlex

Private and corporate interests continue to find new ways to penetrate public K-12 education in Canada, often through the outsourcing of services that are best provided by school-based professionals. In this article, we explore how neoliberal privatization intersects with the discourse of white corporate saviourism through philanthropic non-profit organizations that infiltrate schools under the guise of goodwill and benevolence. Using Project 11 (a mental health non-profit program largely funded by the organization who owns the Winnipeg Jets) as an example of this phenomenon, we illustrate how such organizations may embed themselves in under-resourced schools —made vulnerable by neoliberal reforms— while simultaneously reaping additional social and economic benefits for their involvement. We caution that while programs such as Project 11 may appear —or indeed be— well intentioned, we must maintain critical vigilance against the creeping of white corporate saviourism into our schools. These programs often personalize deeper structural issues, advance corporate interests, reinforce neoliberal ideologies, diminish the role of educators, and perpetuate whiteness and colonial legacies within public education. The presence of private actors within public schools must be scrutinized for the ways in which they may attempt to reshape education to benefit corporate agendas rather than the public good.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.363
Teacher spread0.316 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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