Theorising decolonisation in the context of lifelong learning and transnational migration: anti-colonial and anti-racist perspectives
Bibliographic record
Abstract
In the age of transnational migration, the practices and policies of lifelong learning in many immigrant-receiving countries continue to be impacted by the cultural and discursive politics of colonial legacies. Drawing on a wide range of anti-colonial and anti-racist scholarship, we argue for an approach to lifelong learning that aims to decolonise the ideological underpinnings of colonial relations of rule, especially in terms of its racialised privileging of ‘whiteness’ and Eurocentrism. In the context of lifelong learning, decolonisation would achieve four important purposes. First, it would illustrate the nexus between knowledge, power, and colonial narratives by interrogating how knowledge-making is a fundamental aspect of ‘coloniality’. Second, decolonisation would entail challenging the hegemony of western knowledge, education, and credentials and upholding a ‘multiculturalism of knowledge’ that is inclusive and responsive to the cultural needs and values of transnational migrants. Third, decolonisation would lead to the need for planning and designing learning curricula as well as institutionalised pedagogy based on non-western knowledge systems and epistemic diversity. The final emphasis is on the urgency to decolonise our minds as lifelong learners, practitioners and policy-makers in order to challenge the passivity, colonisation, and marginalisation of learners both in classrooms and workplaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.096 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".