The development of occupational science outside the Anglophone sphere: Enacting global collaboration
Bibliographic record
Abstract
The emergence of occupational science in non-English speaking countries is frequently hampered by diverse barriers to global collaboration, knowledge dissemination, and inclusion in international dialogue. Epistemological, cultural, and institutional resources may explain these barriers, yet these have not been explored within the discipline. This paper discusses three main issues and three priorities for action put forward by participants during sessions held at two separate, international occupational science conferences. The sessions aimed to engage the audience in critical reflexivity and dialogue around the challenges present when non-English speaking countries attempt to develop occupational science scholarship and possible ways to support global collaboration. To stimulate discussion, we used a participatory methodology, ‘Metaplan’. The sessions included a statements exercise, reflections presented by the authors, individual reflexivity, and small group debate. The findings are structured as a reflexive dialogue where participants’ voices, theory, and the authors’ reflections are interwoven to enrich discussion of the issues participants identified and priorities for action. This paper contributes to decolonizing the development of occupational science and promoting an international dialogue that is open to diverse worldviews, by drawing attention to the visible and invisible barriers that limit collaboration and inclusion of the diverse ways in which occupation is understood and enacted worldwide.TAMBIÉN PUBLICADO EN ESPAÑOL https://doi.org/10.1080/14427591.2018.1551048
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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 | 0.062 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".