Troubling Inclusion: The Politics of Peer Work and 'People with Lived Experience' in Mental Health Interventions
Bibliographic record
Abstract
Abstract This thesis is a study of how some mad people come to be known as ‘people with lived experience,’ an emerging identity and strategic essentialism which attempts to valorize the knowledge of those with experiences of distress/mental health system encounters. Currently, claiming such an identity authorizes us to work professionally as peer workers within mental health research and service systems. Thus, by virtue of our ‘lived experience,’ the peer worker becomes enmeshed in the governance of ‘similar others.’ This study maps the emergence, performance, and performativity of the peer worker through the case study of the At Home/Chez Soi project (2009-2013), a national research demonstration project which both implemented services and studied their effects to learn how to best manage the ‘chronically homeless mentally ill’ in Canada. Because peer inclusion is now considered a best practice in mental health interventions, peer workers are key paraprofessionals recruited to be part of the project assemblage. Through ethnographic and interview data, I offer a critical analysis of how peer participation is mobilized and put to work within the confines of mental health governances. By demonstrating how peers actively work to self-govern our subjectivity and subject-positions to become recognizable as peers, this work denaturalizes peer identity. I argue that peer work is ‘bridge work:’ we work as informants to bridge the divides between respectable and degenerate bodies in order to help inform neoliberal governance. Key to this process is peer storytelling, a central way in which our experiences become commodities, consumed as recovery narratives which help maintain us as the problems that need to be fixed. The study elaborates two main conclusions that elucidate a paradox inherent to peer work. First, that our participation is conceived as useful when the target of our experiential knowledge is directed at managing abject populations. Secondly, that when we make attempts to deploy our knowledge to challenge the regimes of truth and practices that govern us, this work is troubled and managed. In this way, peers workers, through practice, learn the rules of engagement that govern our inclusion within the folds of systems of power.
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 | 0.054 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.048 | 0.100 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.007 | 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".