Bibliographic record
Abstract
The Auntie Dialogues is a special journal issue of "The Auntie Is In" podcast scripts. This decolonial approach to research dissemination is aimed at layering Indigenous storytelling alongside written literature. In the sixteen episodes presented in Season One, Dr. Paulina Johnson, or the Auntie, bridges her approach with an “Auntie” mentality to address misconceptions and stereotypes about Indigenous peoples and cultures and better inform listeners of Indigenous realities. The podcast does not solely look at damage or deficit but also the vibrancy of Nehiyawak culture including knowledge relating to creation stories, traditions, ceremonies, and much more. Dr. Johnson is Nêhiyaw or Paskwâw-iyiniw, four-spirit or a prairie person, from Nipisihkopahk, Samson Cree Nation in Maskwacis, Alberta. The Nehiyawak are an oral culture, meaning they share information, through stories, songs, and everyday conversations, and the podcast allows Dr. Johnson to maintain that connection to her people and how knowledge can be shared and importantly to be as straightforward as needed as your own auntie would be to you. By grounding each podcast episode in ceremony and sharing the oral narratives and her own stories and experiences Dr. Johnson is able to facilitate the learning and unlearning needed for decolonization and importantly, reconciliation. These articles are the dialogues of the Auntie is in.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.013 |
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".