Bibliographic record
Abstract
The Zimbabwe government introduced a new language policy in education to change the colonial language policy seven years after attaining independence. So much was expected from the postcolonial language. The use of English as the media of instruction during the colonial era was problematic. It denied Africans to describe the world in their languages. Native languages were marginalized and neglected. Africans were robbed of their self-worth and identity. It is against this background that the Zimbabwean government African states after attaining independence and sovereignty pursued an agenda of linguistic decolonization. This paper evaluates the implementation of Zimbabwe's language policy after it gained independence from Britain in 1980. We argue that despite the claim by the Zimbabwe government that it is a revolutionary government which would completely overhaul all colonial structures, institutions, and policies, the implementation of the language policy is a continuity, rather a radical change. Colonial language policy fundamentals are intact and present in the current language policy. English is still the dominant language of instruction. Indigenous languages are considered inferior and on the verge of extinction. The policy failed where it matters most—decolonizing the mind. Zimbabwe needs a sound language policy in education to shake off vestiges of a colonial legacy, and allow children to go to school in their languages to achieve the overall goal of education for all. The language policy must be developed through a broad-based consultative process with specific implementation strategies and commitment by government and non-governmental agencies for funding its implementation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".