A South African University Funding Model and its Contribution to Transformation Agenda
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".