Phenomenology and the Study of Human Occupation
Bibliographic record
Abstract
Human occupation is the central construct of concern for occupational scientists, yet questions of how it should best be studied are only beginning to be debated in the literature. This article suggests that phenomenology holds promise as a methodological approach for the study of human occupation, and that further discussions about phenomenology in the field of occupational science are warranted. An examination of phenomenology as a methodology for the study of human occupation is undertaken, drawing primarily on the contributions of three major phenomenological philosophers: Edmund Husserl, Martin Heidegger, and Maurice Merleau-Ponty. Five key dimensions of phenomenology, and its generative possibilities as a research methodology for the study of human occupation, are examined. These include: (a) a re-conceptualization of knowledge generation, (b) intentionality and the lifeworld, (c) the notion of Being, (d) the lived body, and (e) the potential of phenomenology to reveal critical insights. This last dimension suggests that modern variants of the phenomenological tradition may have lost sight of the critical origins of the philosophy. This examination considers a complex methodology that has potential to contribute to scholarly conversations concerning the study of human occupation, and to illuminate the centrality of occupation in everyday life.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".