Building knowledge of occupation from the ground up: A field in search of epistemic fairness and social relevance
Bibliographic record
Abstract
In this keynote address to the first World Occupational Science Conference, I share some of the questions I have been grappling with, in the hope that others will perhaps point to alternatives in the way we look for solutions to the dire global context in which we find ourselves in 2022. The questions concern whether contemporary societies need the knowledge occupational science is generating, that is, whether it is useful in explaining the changes, emergent patterns, risks, conflicts, and unfair distribution that trouble societies. Can we be relevant to social change? What I try to reflect on is how to validate embodied, subjective, holistic, and collective forms of knowledge that are solidary and inclusively built, a truly integrated model of knowing, and yet not risk the celebrated (and pursued) scientific status. In addressing these questions, I point to the interconnection of colonization and science, which has largely disregarded Indigenous knowledge methodologies, artisanal knowledge, the intersection of thoughts and feelings, and ciencia nativa (native science) to name a few. I also pose the challenging questions of whether we support occupational scientists while they try to consolidate challenging new lines of research, and how many compromises we should accept to achieve the scientific status that is similar to well-established fields.
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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 | 0.060 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.025 | 0.157 |
| Scholarly communication | 0.033 | 0.055 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".