Decolonising the Curriculum - Transnational Perspectives
Bibliographic record
Abstract
From the Rhodes Must Fall campaign at the University of Cape Town (and, subsequently, the University of Oxford) to the Why is My Curriculum White? debate at University College London, decolonising the curriculum is a hot topic in education. The decolonising the curriculum movement is engaged in an epistemological struggle: it has fostered support and action for change in schools, colleges and universities, but has also met with resistance to the challenges it poses. As Dorie Chetty discusses, the demand for the decolonisation of the curriculum is not new, but today it has greater momentum thanks to a greater sense of awareness among students from minority- ethnic groups who want to see their experiences reflected in the curriculum. This view is reinforced by Nighet Riaz, who explains why a decolonised curriculum can support opportunities for black and minority-ethnic educators and students to understand who they are. Pere Ayling, in her article, writes about how this can be achieved by adopting and utilising the colonial habitus to explore how dominant discourses, operating at the subconscious level, shape and influence our thoughts, actions and perceptions. Decolonising the curriculum takes deliberate effort. Educators need to recognise that the curriculum largely reflects the dominate social group, and therefore can establish a narrow, monocultural view of the world in which ‘others’ exist only on the margins. Decolonising the curriculum is therefore about seeing and appreciating the world – past, present and future – by ensuring that the views and voices of marginalised groups are heard, acknowledged and appreciated. Such an approach benefits all members of society. Today’s educators and academics are detecting and exposing the competing ideological and political motives behind national history curriculum design. Rebecca Harris’s article is an example of this work: in it, Anglocentrism, ‘whiteness’ and ‘white privilege’ are challenged by a call for the diversity of human experience to be at the forefront of history curricula, and for teachers to be trained as ‘critical curriculum thinkers’. These ideas are picked up by Marlon Moncrieffe in his article, which provides an example of decolonising the narrative of mass- migration in the key stage 2 history curriculum. The piece by Richard Race also exposes and reflects upon the integrationist and monocultural design of the national curriculum over the years, arguing that the original national curriculum in England, introduced in 1988, ‘was a Brexit policy 30 years before Brexit’. An exposure of inequitable forms of monocultural nationalism and national identity in Thailand is articulated by Thithimadee Arphattananon. She shares a picture of curriculum inertia and an unchallenged agenda of cultural assimilation in education, fuelled by a teaching of ‘Thai-ness’ that imposes and reproduces a national identity. This type of monocultural dominance of curriculum content and supporting textbooks is also a concern for Kamil Nasibullov and Nataliia Kopylova, who use examples from their research on school music textbooks in Russia to discuss how cultural representations of ethnic Russians have become increasingly dominant, at the expense of minority- ethnic groups. The final article in this collection, by Shirley Steinburg from Canada, champions the notion of decolonising the curriculum while posing a multitude of critical and challenging questions about precisely where and how this process should begin.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".