Bibliographic record
Abstract
Andrea Currie, Green Turtle Woman (Red River Métis) speaks to world-making as the collective work of Indigenous peoples for generations in the face of apocalyptic destruction of their worlds. She positions Traditional Teachings as what will enable her and other Indigenous Peoples to survive, heal, reconstitute themselves, and continue to be who they are in the new reality: “As the world around us dis-integrates into intensifying divisiveness and despair, and our connection to, and interdependence with, our Mother, the Earth, is either denigrated or completely forgotten, I ground myself, every day, in these Teachings.” Andrea then shares what she has learned about living a good life through the Seven Sacred Teachings given to her by the late Murdena Marshall, beloved Mi’kmaw Elder from Eskasoni First Nation. These land and Nation-based Teachings are contextualized within community and relationships. Elder Murdena Marshall taught her that Love is the most important of the Seven Sacred Teachings, followed by honesty, respect, humility, truth, patience, and wisdom. All seven are interconnected, and none can exist without the others. As a storyteller Andrea weaves these Teachings together with her lived experiences; her embeddedness Indigenous communities; and her ways of being and relating as a psychotherapist who has worked in the Mi'kmaq community for over twenty years.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".