Bibliographic record
Abstract
There is a scholarly gap regarding how to cite Nda-nwendaaganag (All My Relations) in academic writing. Nda-nwendaaganag encompasses both the animate and inanimate beings referred to as ‘All My Relations’. I have devised several ways for Indigenous scholars to refer to "All My Relations," because they are our collaborators, co-creators, and idea generators. The purpose is we acknowledge, honor, respect, celebrate, and to express our gratitude to Nda-nwendaaganag as contributors to our written work. In order to create the various methods for Nda-nwendaaganag, I gained motivation from the Baawaajige (Dream) Methods for citation inspiration (Shawanda, 2020). It was considering the critical moments in our time to record the source of knowledge, sacred space, and within seasons to capture when "All My Relations" shared and guided our knowledge. I categorize citation sources from an Anishinaabk worldview on the animate or inanimate. They are distinct in terms of how the information should be recorded for citation purposes. This includes things like the time of day/season, specific locations, and the geography of the generated theme, message, or idea. This will provide Indigenous students, scholars, and researchers to further decolonize settler knowledge systems. I hope that this resource will be useful to students, scholars, and researchers. I understand that it will not be accepted as valid citation practice within western sources, but it is a first step toward honouring and acknowledging Nda-nwendaaganag.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.154 | 0.064 |
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".