Bibliographic record
Abstract
In ‘Weaving a Human-Centric Tapestry,’ Diane Umuhoza Rudakenga shares her personal and professional journey, emphasizing the importance of a human-centric approach to mental health and neurodiversity. Born in Burundi to Rwandan refugee parents, she faced challenges of displacement, racism, and exclusion after moving to Québec City – shaping her resilience and empathy. Diane’s narrative also reveals her late diagnosis of attention deficit hyperactivity disorder (ADHD), underscoring the complexities of neurodiversity, particularly in women. Her professional background in psychoeducation, psychology, human resources, and positive psychology reflects her commitment to diversity, equity, inclusion, and belonging (DEIB). Diane integrates her Rwandan heritage and indigenous perspectives, offering a holistic, communal approach to mental health. She explores intersectionality, racial trauma, and the importance of cultural context in mental health care, drawing on works by Kimberlé Crenshaw, Kathleen G. Nadeau, Monica Williams, and others. Her story highlights the challenges and strengths of being a black, cisgender woman with African Canadian heritage, advocating for understanding the full spectrum of human diversity. Diane concludes by advocating for inclusivity and empathy, emphasizing the value of diverse experiences in enriching the human tapestry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".