Recovering in place: what the concept of place can offer the field of recovery science
Bibliographic record
Abstract
Recovery science has traditionally focused on individual change, with less attention to the environments that make recovery possible. Socio-ecological models have advanced understanding of contextual factors but have not fully engaged with how recovery is experienced in and through place. Drawing on phenomenological and critical geographical perspectives, this think piece introduces the Recovering in Place Model, a framework that explains how physical, emotional, and socio-political dimensions of place interact to shape recovery. The model extends recovery-oriented systems of care (ROSC) and recovery capital (RC) by situating them within spatial contexts, showing how environments can both foster and hinder ontological security, belonging, and the experience of 'home' in recovery. Examples from Collegiate Recovery Programs and Inclusive Recovery Cities illustrate how recovery unfolds across scales, from campus lounges to city streets, through place-based processes that can either support or constrain recovery. The think piece concludes by outlining emerging approaches for measuring recovering in place, including adaptations of recovery capital, place attachment, and recovery ecosystem indices, to better capture how environments co-create the conditions for sustained recovery. By integrating phenomenological and critical geographic perspectives, it calls for a spatial turn in recovery science, providing implications for research, policy, and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.148 |
| Scholarly communication | 0.017 | 0.041 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".