Integrating Postcolonial Culture(s) into Primary English Language Teaching
Bibliographic record
Abstract
This article argues in favour of using picturebooks to extend the limited view of cultural learning that is entailed in textbooks for teaching English at primary school. A critical view looking into textbooks for primary English education reveals that despite the general recognition of postcolonial literatures and cultures, target culture input almost only refers to the UK and the USA. Furthermore, the information students receive is over-generalising and stereotypical and does not pay tribute to the diversity of postcolonial cultures. This paper suggests that postcolonial literatures can be shared with primary English learners to broaden their perception of the English-speaking world. In view of the marginalized representation of these literatures and thus their respective cultures in ELT, this paper suggests closing the gap by using picturebooks from the Inuit and from Kenya and India. A comparison of two picturebooks from each geographical area reveals that one of these needs to be seen critically for its representation of cultural identity, whereas the other can be recommended for enhancing intercultural learning. As a conclusion, the article offers guiding questions of how to select postcolonial literature picturebooks to afford access to diverse cultures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".