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Record W4389427123 · doi:10.21083/ajote.v12i2.7519

Against the pedagogy of debt in South African higher education

2023· article· en· W4389427123 on OpenAlexvenueno aff
Mukovhe Masutha

Bibliographic record

VenueAfrican Journal of Teacher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsAusterityDebtStudent debtGovernment (linguistics)KwameDisenchantmentOrthodoxySociologyLoanPolitical sciencePublic administrationPolitical economyEconomicsLawTheologyFinance

Abstract

fetched live from OpenAlex

On the back of decades of austerity, marketisation, credentialization and related neoliberal conceptions of education and society, a student debt crisis has emerged in higher education (HE). Despite the well-documented history of government-guaranteed income contingent loans (ICLs) indenturing students and their present and future families, such loans continue to be canvassed by policymakers and interest groups as an ideal ladder of educational opportunity, particularly for students from traditionally excluded communities. In this paper, the author brings together insights from Jeffrey Williams’ Pedagogy of Debt, Carter G Woodson’s Miseducation, Ha-Joon Chang’s idea of Bad Samaritans, and Kwame Nkrumah’s theory of Sham Independence as conceptual building blocks to reinforce the wall of resistance against the orthodoxy of debt as a paradigm for HE funding in South Africa. To add to the student debt abolition movements and the voices calling for freeing public HE, this paper critically reviews the recommendations of South Africa’s 2017 Fees Commission Report. This is done to offer an analysis that makes explicit the likely impact of the proposed student loan policy on South Africa. As we imagine transitioning towards the new African University, this paper makes a case for freeing public HE for all, on the basis of mutual aid, transitional and reparative justice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.062
GPT teacher head0.390
Teacher spread0.327 · 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 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
Published2023
Admission routes1
Has abstractyes

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