Critical Ethnographic Analysis of “Doing Good” on Short‐Term International Immersion Experiences
Bibliographic record
Abstract
Reciprocal partnerships are growing alongside the rise of international learning and "doing" experiences for students and clinicians. This paper questions how global citizenship, the acquisition of awareness and skills to sensitively navigate through a rapidly globalized social world, is cultivated amidst international partnerships focused on short-term immersion opportunities. Using an ethnographic methodology to examine the experiences of occupational therapy students abroad, this paper addresses the potential for competing agendas when the motivation to participate within these partnerships is driven in part by a desire to "do good." The empirical lens was directed towards the students' verbal, written and enacted narratives rather than the sociocultural realm of the sending institution, the host organization or the occupational realities of the local communities, therefore is limited in discursive scope. Nevertheless, the need is great for further critical appraisal of objectives and expectations by all parties to foster a partnership culture of reciprocity and equality and to diminish the neocolonial legacy of Western expertise dissemination. By examining how the stated and implied desire to do good exists alongside the risk to do harm to individuals and international networks, the conclusions can be extended locally to highlight the challenges to "partnering up" between clinicians and patients.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".