Postcolonial history education: Issues, tensions and opportunities
Bibliographic record
Abstract
This paper introduces a journal special issue devoted to an exploration of post-colonial history education with contributions from Ghana, Uganda, New Zealand, Canada, Botswana, Nigeria, Cyprus, Lebanon and London. It provides an overview of key issues, tensions and opportunities around decolonising the history curriculum. Relevant contexts such as the ‘History Wars’, subaltern studies, the conception of decolonising the mind and the possibilities of de-colonising pedagogies are explored. History education lenses around critical historical literacy, historical consciousness, multidimensional identities and multi-perspectivity are brought to bear upon the question of re-thinking forms of postcolonial history education. Specific political circumstances inform the nature of history education in every national jurisdiction; here the contemporary Black Lives Matter campaign, the fallout from the mismanagement of the fate of the ‘Windrush’ settlers in the UK and the recent focus of protestors globally upon colonial oppressors memorialised in statues frame the authors’ reflections. However, echoing the optimism of most of the special issue contributions, opportunities to build bridges between divided communities, open up more inclusive history curricula to student voices and nuance and complicate homogeneous national narratives are identified and recommended.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".