The Importance of Understanding Culture When Improving Education: Learning from Cambodia
Bibliographic record
Abstract
Following Education for All, initiatives like child-friendly schools initiative is rolled out in many countries, including Cambodia. The child-friendly schools initiative is addressing general and local needs of children in schools, like a safe environment, well-trained teachers and the provision of teaching and materials. But there is also a component that is more cultural in nature and might not resonate well with the country’s culture. As Hofstede’s (1980, 1986) research concerning cultural differences indicated, a country’s culture can be described following five dimensions (individualism/collectivism, power distance, uncertainty avoidance, masculinity/femininity, and long-term/short-term orientation). Not taking a country’s culture into account while intervening with important services like education, might lead to low intervention outcomes, teachers who feel uncomfortable with the proposed contents and ways of teaching, and students who are not prepared well towards the society they live in. A cultural profile of Cambodia was missing when the ministry of education started to roll out the Education for All and Child Friendly School approaches in 2006/2007 (Schaeffer & Heng, 2016). The original research has since been enriched with additional data sets. The data sets are by no means large enough to be representative, but through triangulation a careful attempt is made to at least inform educationalists of the importance of taking culture into account when designing and implementing educational interventions to improve learning in Cambodia, and likely elsewhere. With the onset of the Sustainable Development Goals (UN Sustainable Development Knowledge Platform, 2015), such cultural understanding is a necessity in order to achieve cultural appropriate project outcomes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".