Bibliographic record
Abstract
Within this particular issue of American Indian Quarterly, I expect that there will be stories that speak about incidences of racism, intolerance, exclusion, and ignorance as experienced by Native scholars within academia. My experiences as a non-Native student in a graduate program in Native studies are reflective of my experiences within Native communities. The story that I have to share is one that speaks almost entirely about welcoming, acceptance, inclusion, and support. The challenges that I have faced, to this point in time, have for the most part arisen inside myself. My choice to pursue a PhD in Native studies brought the underlying issue-What is my place, my role, in the discipline of Native studies?-to the surface once again. I am a PhD candidate in the Department of Native Studies at Trent University. My return to student life began in 2001, after more than a decade had passed since the completion of my master's degree. My decision to become part of this particular program was based on three factors. First of all, it is at this time the only doctoral program in Canada in the discipline of Native studies. Second, my learning experiences as an undergraduate within this same department more than two decades earlier had continued to be valuable in my personal and professional life. Finally, and most importantly, this particular program respects Native ways of knowing as reflected in traditional and contemporary perspectives. Though this is a relatively new program-when I started it was beginning its third year of operation-it is part of one of the longest-standing Native studies departments in North America. The majority of faculty and staff within the department are Native people. Within the doctoral program the ratio of Native to non-Native students is roughly two to one.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".