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Enregistrement W4411195228 · doi:10.5430/ijhe.v14n3p1

A South African University Funding Model and its Contribution to Transformation Agenda

2025· article· en· W4411195228 sur OpenAlexvenueno aff
Dr Oliver Jan Mbhalati

Notice bibliographique

RevueInternational Journal of Higher Education · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHigher Education Governance and Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTransformation (genetics)Political scienceRegional sciencePublic administrationSociologyChemistry

Résumé

récupéré en direct d'OpenAlex

In this paper I report on a study which investigated a South African university funding model and its impact on the country’s socioeconomic transformation agenda. The main objective of the study was to develop a South African university funding model that would align with the country’s transformation agenda. The data presented in the paper were sourced using a combination of quantitative and qualitative research design methods. The quantitative approach involved 160 questionnaires, and 17 respondents participated in interviews as part of the qualitative research process. The respondents were purposively selected from six groupings which included the recently employed former National Student Financial Aid Scheme (NSFAS) funded students, Department of Higher Education and Training officials, NSFAS officials, Students’ Representative Council (SRC) members, and officials employed in the finance divisions and registrar’s offices at public universities in South Africa. The results demonstrated that amongst the various sources of funding for the South African university sector, government and NSFAS funding were significantly contributing to the transformation agenda in South Africa. The findings confirm the agency theory perspective that funding is a tool that governments use to entice universities towards the achievement of the transformation agenda.In this paper I report [AB1] on[AB2] a study[AB3] which investigated a South African university funding model and its impact on the country’s s[AB4] ocioeconomic transformation agenda. The main objective of the study was to develop a South African university funding model that would align with the country’s transformation agenda. The data presented in the [MOU5] [OM6] paper w[AB7] ere sourced[AB8] using a combination of quantitative and qualitative research design methods. The quantitative approach involved 160 questionnaires, and 17 respondents participated in interviews as part of the qualitative research process[MOU9] [OM10] . The respondents were purposively selected from six groupings [AB11] [OM12] which included the recently employed former National Student Financial Aid Scheme (NSFAS) funded students, Department of Higher Education and Training[AB13] [OM14] officials, NSFAS officials, Students’ Representative Council[AB15] [OM16] (SRC) members,[AB17] [OM18] and officials employed in the finance divisions and registrar’s offices at public universities in South Africa. The results demonstrated that amongst the various sources of funding for the South African university sector, government and NSFAS funding were significantly contributing to the transformation agenda in South Africa. The findings confirm the agency theory perspective that funding is a tool that governments use [AB19] [OM20] to entice universities towards[AB21] [OM22] the achievement of the transformation agenda.[MOU23] [OM24] [AB1]You (or “we”) report; the paper does not. APA prefers to avoid use of anthropomorphism (attributing human qualities to nonhuman things). APA permits use of first-person pronouns (e.g., I, we). [AB2]Consider being more specific. “evaluate”? “analyse”? “investigate”? [AB3]It is not clear which researcher you are referring to. Consider using the researcher’s name and publication year. Or, do you mean “In this study I investigate a South African university . . .” If so, to avoid confusion and to adhere to APA guidelines, refer to yourself as “I.” [AB4]APA closes the prefix “socio.” [MOU5]Add the research objectives to be studied in full. [OM6]Done [AB7]“Data” is plural. [AB8]If you sourced the data, use “I sourced the data in the paper . . .” [MOU9]Add quantitative and qualitative results [OM10]Added [AB11] [AB11]If you selected the respondents, use “I selected the respondents from six groupings using purposive sampling . . .” [OM12]accept [AB13]Use the full name. The initialism is not needed because it is not used again in the abstract. [OM14]Done [AB15]Use the full name. The initialism is not needed because it is not used again in the abstract. [OM16]Done [AB17]APA uses the serial comma, regardless of English style. [OM18]agreed [AB19]Revised for active voice, which APA prefers. [OM20]Accept [AB21]Per your request, I have retained use of UK English/spelling, but please note that APA prefers use of US English. [OM22]Noted [MOU23]Write down your findings according to the goals you want to achieve. [OM24]Links to the research objectives

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,018
score de la tête « metaresearch » (Gemma)0,027
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,183

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0180,027
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0040,006
Études des sciences et des technologies0,0120,017
Communication savante0,0200,014
Science ouverte0,0020,014
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,339
Écart entre enseignants0,316 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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