The SDG agenda and university transformation in Africa: The decolonial turn deferred?
Bibliographic record
Abstract
The Sustainable Development Goals (SDGs) agreed in September 2015 set the scene for a renewed and ambitious development framework in a global context of widening inequalities within and between countries, global economic crises, conflict and climate change. Higher education is framed in several of the targets that make up SDG 4 and is also argued as central to achieving all 17 goals. However, the extent to which they engage with higher education in the context of calls for responsive, decolonised higher education remains underexplored. It is this gap that this paper addresses, arguing that while the SDGs take a broad approach to education the focus on specific targets and indicators limit states’ autonomy by de-territorialising local frameworks (Sayed & Moriarty, 2020). As a result, universities in Africa struggle to assert their agendas as power is overly located at the supra national level. We use the case of South African higher education to examine how and in what ways the national education agenda articulates with the SDG agenda. In particular, we focus on the lack of a clear equity and anti-racist focus in the SDG agenda which fails to engage with the disciplinary hold of racism over knowledge. We use Boaventura de Sousa Santos’ (2014) notion of difficult questions in higher education with weak answers to address what a decolonised and deracialised higher education system might look like. In particular, we articulate how Western domination has marginalised knowledge present in the global South. In so doing, we describe the influence of the SDGs in higher education noting the strides made but also their limited application in the global South and the decolonial turn. We argue that the decolonisation of knowledge in higher education is a collective process in which disruptive disciplinary practices contribute to cognitive global justice.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.046 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".