Bibliographic record
Abstract
In this project report, I introduce the citation templates for Indigenous Elders and Knowledge Keepers that I created in partnership with the staff of the NorQuest Indigenous Student Centre. These citation templates have been adopted/linked to by twenty-five institutions across Canada and the United States. They represent an attempt to formalize something that Indigenous scholars have been doing for decades: fighting to find a better way to acknowledge our voices and knowledges within academia. I outline how the project was developed, highlighting the importance of stable, respectful relationships, before delving into some of the literature and personal experiences that provided the reasoning for why more culturally responsive citation is needed. Part of the background is acknowledging my own experiences as an Indigenous scholar, but I also draw on literature from both Indigenous and non-Indigenous scholars to illustrate the interdisciplinary need for these templates. I provide in-depth explanations of each element in the new citation templates to explain the reasoning behind and/or importance of each element. For example, I outline why including the individual’s nation/community is important for breaking down the pan-Indigenous stereotype and helping scholars to recognize the variation of knowledge across the hundreds of unique Indigenous communities. While the main focus of this paper will be these specific citation templates, I hope that it will also empower, inspire, and provide a case study of how academia can make small changes to improve the respectful recognition of Indigenous knowledges and voices. Given the recent focus in educational institutions on being more inclusive of Indigenous ways of knowing, I think it is only right that we also look at reconsidering how we treat things like Indigenous oral knowledge in academia and whether there are systems in place that implicitly prioritize written knowledge over oral knowledge in a form of ongoing colonialism.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".