Constructing English as a Ugandan language through an English textbook
Bibliographic record
Abstract
English is a national language in Uganda and is widely used in elite areas such as politics and business, but most Ugandans master English to only a limited degree. In this situation, English can be seen as either a foreign language or a second language – influencing how English is taught. One goal of language teaching espoused in this article is for students to develop intercultural communicative competence, and the article discusses the extent to which text and images invite conversations and discussions on culture and English as an international language. The framework for analysis is Weninger and Kiss's [2013. Culture in English as a foreign language (EFL) textbooks: A semiotic approach. TESOL Quarterly. doi:10.1002/tesq.87] framework for a Peircean semiotic approach to textual and visual analysis, and the material chosen for the study is a Ugandan English secondary textbook. The textbook has few references to foreign culture or English as an international language, and these references do not lend themselves to critical or exploratory queries or discussions on these issues. This paucity can be seen as an expression of the desire to emphasise English as a Ugandan language, and as such the textbook serves nationalistic purposes, a finding shared by other research. Teachers should be cognisant of the potential of images for spurring curiosity and discussions in the classroom.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".