Towards Designing an Intercultural Curriculum: A Case Study from the Atlantic Coast of Nicaragua
Bibliographic record
Abstract
One of the challenges still to be met in the 21st century is that of genuinely embracing diversity. How can education help to overcome the barriers that continue to exist between people on the basis of language, culture and gender? This case study takes the Atlantic Coast of Nicaragua as an example of a multilingual/multiethnic region and examines how the community university URACCAN is contributing to the development of interculturality. It describes participatory research that was carried out with university staff and students with the intention of defining an intercultural curriculum and appropriate strategies for delivering such. One model used as a basis for discussions was the Model for Community Understanding from the Wales Curriculum Council, which emphasises the belonging of the individual to different communities or cultures at the same time. Factors supporting the development of an intercultural curriculum include the university’s close involvement with the ethnic communities it serves. However, ethno-linguistic power relations within the region and the country as a whole, still militate against egalitarianism within the university. The research highlights the importance of participatory pedagogy as the basis for promoting interculturality and achieving lasting social transformation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".