Mino-Bimaadiziwin and the Pursuit of Harmony
Bibliographic record
Abstract
Academic AbstractTheories of well-being are heavily influenced by Western viewpoints; even when Majority World perspectives are incorporated, psychologists often fail to consider the rich cultural knowledge of the Indigenous peoples of North America. We propose that personality and social psychologists engage in "deep learning" (without appropriation or colonization) of a Native American Majority World perspective on well-being. We present a description of the "storywork" surrounding the concept of Mino-Bimaadiziwin, translated as "the good life" or "living in a good way," emerging for the Anishinaabeg, who are Indigenous peoples consisting of the Ojibwe (Chippewa), Potawatomi, Algonquin, Saulteaux, Nipissing, and Mississauga First Nations. We discuss the Gichi-dibaakonigewinan, The Seven Grandfather Teachings, that emerge to promote well-being, which is inherently relational and concerns harmony in contrast to happiness. Gichi-dibaakonigewinan are composed of seven virtues-Truth, Humility, Respect, Love, Bravery, Integrity, and Wisdom-whose consideration reveals pathways to well-being possible in Majority World Contexts that psychologists should consider.Public AbstractWhat is the good life, or living in a good way? Western psychology, rooted in Western philosophy, has sought to study this very question. However, researchers have not considered the perspective of the Majority World. In this paper, we present how the Anishinaabeg, who are Indigenous peoples consisting of the Ojibwe (Chippewa), Potawatomi, Algonquin, Saulteaux, Nipissing, and Mississauga First Nations, think about the good life, or Mino-Bimaadiziwin. Mino-Bimaadiziwin is characterized by the pursuit of harmony in contrast to happiness. We discuss the Gichi-dibaakonigewinan, The Seven Grandfather Teachings, that promote well-being. These Seven Grandfather Teachings are composed of seven virtues, each typified by an animal-the turtle for Truth, wolf for Humility, buffalo for Respect, eagle for Love, Bear for Courage, Raven for Integrity, and Beaver for Wisdom-whose consideration reveals pathways to well-being possible in Majority World Contexts psychologists should consider.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".