Fieldwork Experience as Cultural Immersion: Two International Students and Their Professor Reflect on a Recent Evaluation Practicum
Bibliographic record
Abstract
Abstract: In this practice note, two international students of evaluation reflect on the cultural challenges they experienced learning to apply Western-based methodologies in unfamiliar cultural contexts. Using a process of reflection, the students identify four interrelated challenges: understanding the culture and program setting, the need for interpersonal and communication skills, learning the language of evaluation, and telling the story. The paper concludes with reflections from the course professor, who highlights the challenges of teaching evaluation, an intensely cultural practice with deep roots in Western theoretical traditions, to students who do not come from the United States. Through reflective practice, the two students were able to overcome many of their initial challenges.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".