POLITICIZED NARRATIVE THERAPY
Bibliographic record
Abstract
Using a poly-vocal approach, this piece calls for the interruption and interrogation of narrative therapy’s colonial associations (White & Epston, 1990), and the cooption of narrative therapy by psychiatry under the guise of progressiveness (J. Poole, Personal Communication, January 31, 2017). We locate narrative therapy in the neoliberal geography of recovery and marketization, where social problems are coded as individual struggles, personal stories are used as mental health marketing material, and the burden of wellness enables psychiatric governance (Costa et al., 2012; Morrow, 2013; Poole, 2011). Drawing on Sefa Dei and Asgharzadeh’s (2001) anti-colonial discursive framework, critical race theory and its technique of counter-storytelling, Patricia Hill Collins’ (1990) Black feminist thought, and anti-sanist theorizing, we explore the possibility of reimagining narrative therapy for political ends. Throughout this piece, we draw on narrative techniques to move beyond an individual understanding of distress, connecting personal struggles to the broader social and political context. We do this by extending a political lens to the four steps taken in a mainstream narrative approach. We have chosen to use case studies informed by our own lived experiences in order to highlight the potential that we see in narrative work. This approach does not leave narrative therapy unchallenged and we understand that by remaining in a narrative framework housed in social work practice we cannot truly separate our approach from colonial care (Baskin, 2016; Lee & Ferrer, 2014). Rather, we hope to start a critical and transparent conversation that begins to explore the reconceptualization of narrative therapy for the purpose of deconstructing dominant discourses and making any colonial connections visible.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".