Bibliographic record
Abstract
Abstract Since humanities arise from a specific place and from the people of that place, this article will focus on Peacemaker’s revolutionary teachings about the seed of law. Long before the people from across the ocean arrived here on Turtle Island (North America) there was much warfare happening. According to John Mohawk (2001, para. 1), an Iroquoian social historian, “[t]he people had been at war for so long that some were born knowing they had enemies [but] not knowing why they had enemies”. Peacemaker planted the seeds of peace which resulted in the Kayenla’kowa, the Great Law of Peace (n. d.), which is the basis of the Hotinosh^ni Confederacy. With the burial of the weapons of war under the Great Tree of Peace the Hotinosh^ni were able to develop their rituals and ceremonies to reflect their relationship with creation. This peaceful confederacy was disrupted shortly after the Europeans arrived with their violent imperialistic ways of life. The 1996 Royal Commission on Aboriginal People (RCAP) documented the situation of Aboriginal communities, which was the result of oppressive policies and programs of colonialism. The RCAP also captured the many different voices of the Aboriginal people in their struggle to revitalise their traditional teachings that will make them strong again.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".