Storywork to Decolonize Mental Health: Recentering Indigenous Histories in Canada, Kenya and Australia
Bibliographic record
Abstract
Colonization has had extremely negative impacts on the mental health and wellness of Indigenous peoples throughout the world. In this paper we take up colonial processes as they relate to Indigenous lives and mental health in three contexts: Canada, Kenya and Australia. This work engages storytelling and the method of storywork (Archibald et al., 2019) as a way to preserve and pass on history and as a way of resisting colonial oppression. This work is grounded in an intersectional approach to social justice and decolonization (Crenshaw, 1990; Hankivsky & Cormier, 2011), and supported sharing, knowledge co-creation and joint thematic narrative analysis of Indigenous experiences of mental health and justice systems across the three contexts. Our writing team represents a collaborative process between Indigenous and non-Indigenous authors where the members of the team most impacted by colonization use stories to reflect on the impact of colonization and its specific ties to psychiatric, justice and child welfare systems. This paper is presented in three main parts beginning with “Emile’s Story,” followed by “Remembering ‘Is That You Ruthie?’” and concluding with “Navigating Kenya’s Colonial Legacy.” This work engages a process of decolonization by challenging these destructive colonial narratives through storytelling. This paper will both document and demonstrate the importance of creating space for different forms of knowledge creation within academia.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.049 | 0.035 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".