Bibliographic record
Abstract
This article is a narrative and conceptual exploration of the journey towards practicing Indigenous allyship in an academic context. I begin by tracing a trajectory of coming to work with Indigenous peoples as a non-Indigenous, multiple migrant, and queer person of color situated as a therapist and educator in a Canadian academic institution’s Faculty of Medicine and Health Sciences. Anti-racist and de/postcolonial theories and concepts abound to label my experiences of tokenization, yet they invariably fall short of the nuanced and complex ways that both reconciliation and oppression unfold in the everyday. Beyond critical theories that speak with certainty of structural violence, I trace my trajectory of coming to understand my work with Indigenous peoples within and for healthcare curricula and community development. I describe an intertextual practice of echopoetics that is trying to make sense of a world where both historical trauma and daily aggressions continually reproduce inequities, in order to reveal spaces of possible hope and healing. Yet, what seems to be happening in this echopoetics is a process of unbelonging from the multiple cultural and institutional narratives in my surround—at times including those that intend to liberate. Focusing on the negation—“ non”—as a non-Indigenous/non-White person, I provide a reflection on how this practice cultivates an unbelonging that becomes both a political stance at the point of invisibility, as well as a lonely yet definite healing.
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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.047 | 0.136 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.005 | 0.038 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".