Defining and refining emplacement by deepening the understanding of embeddedness, situatedness, and enactedness
Bibliographic record
Abstract
Using a transactional frame of reference, this paper situates the concept of emplacement within and between the notions of embeddedness, situatedness, and enactedness. Emplacement recognises how the spaces and places in which we engage in occupations both shape and are shaped by engagement. Hence, the meaning and importance attributed to certain occupations can involve a complex, unbounded, uncertain, and often messy process. Further, embeddedness describes the deep anchoring of individuals to their social and physical contexts. Situatedness emphasises the importance of being ‘in-place’. Enactedness delves into the dynamic unfolding of occupations that highlight the role of agency and identity that are reflected in the stories shared. Vignettes from our research projects illustrate these concepts by offering insights into the lived experiences of emplacement. These narratives enrich understandings of the nuanced, transactional nature of emplacement. Additionally, we acknowledge the relevance of emplacement across contexts and suggest avenues for future research. This discussion adds to the existing understanding of emplacement and the growing body of knowledge in occupational science specific to the value of using a transactional frame of occupation to explore the complexities of human-occupation-environment interactions.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".