Adapting Textbooks to Reflect Student Needs in Cambodia and the ASEAN Region
Bibliographic record
Abstract
The authors first discuss the emergence of English as a lingua franca in Cambodia and elsewhere in Southeast Asia, and the emergence of Kirkpatrick's (2011) multilingual model of English teaching in the region.They then consider the importance of textbook adaptation as a way of supporting this new paradigm and the role that non-native-speaking teachers have in creating these adaptations.A detailed example of textbook adaptation, which explains (1) why to consider adapting materials and (2) how to make well-considered, manageable changes, is then provided as a model for practioners to consider.Finally, some practical concerns teachers might have about texbook adaptations are addressed.A Japanese-coordinated meeting between delegates from Cambodia and Colombia to plan training in rural land-mine removal took place in October 2010 in Phnom Penh, Cambodia.No representatives from an English-speaking country participated.In which language was the training held?Not surprisingly, the answer is English.(S.Nem, personal communication, March 20, 2011).This is just one example of how English is already used in Cambodia as a means of communication between people who do not share it as their first language.Such interaction in Cambodia and the other members of ASEAN (Association of Southeast Asian Nations) will continue to grow.This growth is due, at least partially, to (1) the fact that use of English as the organization's sole working language is already mandated (Association of Southeast Asian Nations, 2007) and (2) the promotion of "English as an international business language at the work place" being one objective of ASEAN's plans for regional integration in 2015 (ASEAN Secretariat, 2009, p. 3).Clearly, English use among non-native speakers is taking on an everincreasing role in the spread of professional information in the region.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".