Panjabi-Centred Design: Embracing Transgressive Liberation and Fostering Accessibility in Healthcare and Social Welfare for the Panjabi Communities
Bibliographic record
Abstract
This Major Research Paper (MRP) critically investigates the inadequate relationships between healthcare and social welfare organizations and Panjabi communities in Canada, exposing the colonial foundations that continue to perpetuate systemic marginalization. Present services fail to adequately address the long-standing presence of intergenerational trauma, community-driven practices, and language and access barriers. The provision of translated materials in Panjabi (Gurmukhi) constitutes a reactive approach that perpetuates the illusion of liberation, rather than offering comprehensive care. Despite strategic plans that profess to provide equitable healthcare for all, the Canadian healthcare and social welfare systems remain entrenched in deeply rooted colonial practices that oppress marginalized voices. \nThis MRP presents the Panjabi-Centred Design (PCD) Framework, a culturally-responsive approach that confronts the unique needs and experiences of the Panjabi diaspora within the Canadian healthcare and social welfare systems. By fusing traditional Panjabi values and practices with contemporary design methodologies, the PCD Framework strives to cultivate equity, inclusivity, and well-being for Panjabi communities. The research emphasizes the importance of contemplating factors such as intergenerational trauma, generational family structures, and language barriers in devising proactive solutions while advocating for the dismantling of prevailing reactive and half-measured approaches. \nThe PCD Framework ultimately aspires to achieve transgressive liberation for Panjabi communities and other marginalized populations by contesting colonial practices, holding healthcare authorities accountable, and nurturing community engagement and empowerment.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".