A Review and Synthesis of Current Research on Cross-Cultural Evaluation
Bibliographic record
Abstract
As a fairly new and emergent construct, there remain many gaps in our knowledge about how to integrate notions of culture and cultural context into evaluation theory and practice, as well as gaps in our knowledge about how to conduct and implement evaluations in immigrant and indigenous communities. In this article, the authors provide a comprehensive review of the empirical literature on evaluations conducted in cultural communities, with an emphasis on the relationship between evaluators and stakeholders in the cross-cultural program context. The analysis of the literature selected for review leads to the development of a theoretical framework describing the inter-related and multi-textual dimensions (relational, ecological, methodological, organizational and personal) that interweave throughout the evaluation, and that ultimately inform the relationship between evaluators and stakeholders in the cross-cultural program context. The article concludes with an agenda for future research.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".