Reflexive Reflection Co-created with Kehte-ayak (Old Ones) as an Indigenous Qualitative Methodological Data Contemplation Tool
Bibliographic record
Abstract
The aim of this paper is to propose a new way of understanding data contemplation for Indigenous methodologies. There is a need for Indigenous methods that allow us to explore and organize findings that are steeped in the contextualized story and grounded in the research relationship. A study that asked Cree Kehte-ayak (Old Ones) about the relevance in harmonizing Indigenous and Western ways of knowing in healing from addiction shows that Reflexive Reflection (RR) offers a respectful way for discovery. RR offers epistemological underpinnings for data consideration when engaging Indigenous methodologies. Culturally rooted addictions research can contribute to Indigenous wellness and cultural renewal by bringing awareness to the link between colonialism and addiction and by actively re-centring an Indigenous worldview and governance in the research process (Hall et al., 2015). While challenging colonialism is vital, the strength of Indigenous culture must be central to the overall project, with relational accountability that implies all parts of the research process are related, and that the researcher is responsible for nurturing and maintaining this relationship with the research process and with “all relations.” Indigenous research inquiry involves moments of contemplation that explore dreams, intuition, teachings, and connection to land. It also involves spending intimate hours listening to stories of the “old ones” that are rooted in a sense of kinshipresponsibility that relay culture, identity, and a sense of belonging that are essential to the life of the researcher. Reframing the language around aftercare services for Indigenous Peoples can take place through reflexive investigation and knowledge creation.
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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 | 0.076 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".