Bibliographic record
Abstract
The goal of this article is to expand beyond the western parameters of methodologies that have marginalized Indigenous communities and Ways of Knowing by shifting the paradigm to include Dream Methodologies, a branch of Indigenous Methodologies. This article outlines the Anishinaabek dream-specific protocols for accessing dreams, as well as the guidelines for sharing your Dream Methodology within scholarly work, for Indigenous students, scholars, and researchers. We present this methodology in order to encourage Indigenous people working in academia to prioritize our traditional methods of gaining knowledge. The findings are used to create a reference guide for students who will use their Dream Knowledge in their papers. We hope to encourage Indigenous students, scholars, and researchers to use Dream Methodologies in their work and to access Dreams using their traditional protocols.
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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 | 0.047 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".