Changing the Visual Landscape
Bibliographic record
Abstract
The national #RhodesMustFall and #FeesMustFall student protests of 2015–2016 at universities across South Africa foregrounded the need for the transformation, decolonisation, redress and Africanisation of the country’s higher education institutions. One of the ways that Stellenbosch University (SU) has endeavoured to address transformation-related challenges linked to symbols and names is with the Visual Redress Project, whose aim is to change the visual landscape of the university’s campuses. This paper explores the reactions of students and staff to initiatives carried out thus far by the Visual Redress project on SU’s Stellenbosch campus. It attempts to contribute to the discourse around the transformation of higher education in South Africa through a look at how social cohesion and the sharing of stories and identities could be achieved on SU’s campus through visual redress. It draws upon and expands on the existing research on visual redress conducted at the University (Fataar & Costandius, 2021; Costandius et al., 2020; Clarke & Costandius, 2019). The paper aims not only to provide insight into SU’s transformation efforts but to also use these responses and reactions to potentially inform future transformation imperatives and redress initiatives in particular on this and other campuses locally and globally.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".