Bibliographic record
Abstract
As a Métis person working in the academy, I have responsibilities to my community and my employer. There are times when my Métis values are at odds with the system. This paper serves as an introduction to who I am as a scholar. I outline my priorities and share the philosophical underpinnings of my research. I name some of the challenges that come from navigating identity and expectation, and I celebrate the partnerships that sustain my spirit and ways of being in academia. As part of a strong Métis collective, I am able to focus my time on things that matter to our community. These initiatives nourish my energies and allow me to advance other university-specific requirements in the areas of research, teaching, and service. It is a careful road to navigate and unique to the experiences of Indigenous scholars who are expected to bring their indigeneity to the forefront of who they are in their work. While research faculty are expected devote their time 40% to research tasks, 40% to teaching, and 20% to service, Indigenous scholars might argue that our work is nearly 100% service if we are doing it right.
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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".