Postcolonial Critical Pedagogy of English Language: Formulating Good Practices
Bibliographic record
Abstract
Initially due to colonial invasion, and later through globalization and neocolonial power relations, the English language has increased in its all-pervasive authority. The superior status of English over other languages is maintained in all sectors, including its pedagogy, at the structural level through gatekeeping policies, at the functional level through extending its scope, and the cultural level through the normalized attitudes and professional/social practices relating the language. To mitigate this situation and to advance the cause of linguistic decolonization and linguistic human rights, I intend to derive recommendable good practices for a Postcolonial Critical Pedagogy of English Language (PCPEL). The formulation of PCPEL is carried out by using the constructive grounded theory method under the methodological framework of generic benchmarking for an eclectic selection of quality statements suitable for multiple postcolonial settings. These quality aspects are accumulated from the relevant literature in a cross-matching and iterative or constant-comparative manner through coding, conceptualizing and categorizing. The outcome is a list of twenty quality standards under four quality categories for English language teaching (ELT)—ELT policy essues, ELT principles, ELT content, and ELT methods—which may be used as a quality framework to implement and assess ELT from a postcolonial critical perspective.
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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 | 0.101 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".