Studying occupations across borders: Methodological reflections on the value of cross-national comparative research
Bibliographic record
Abstract
This paper examines the value of cross-national comparative (CNC) studies in advancing understandings of occupation. Drawing from a doctoral CNC ethnographic methodology as an illustrative example, we assert that developing knowledge about occupation across multiple settings can enhance theorization in the discipline in two ways. First, CNC research can respond to the discipline’s call to study the situatedness of occupation and examine macro-level structures critically. Second, CNC research can challenge prevailing assumptions in occupational science and foster the development of theories that are either applicable to multiple sociocultural contexts or explicitly contextualized. This paper focuses on the methodological reflections that emerged when conducting and developing findings in our CNC study. It addresses considerations for rigour in this type of research, including case selection, ensuring comparability, navigating varying positionalities across sites, and mitigating the reproduction of dominant national discourses and methodological nationalism.
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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 | 0.149 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 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".