Wisdom of the Ages: From Houses to Monsters, the Naming Practices of the
Bibliographic record
Abstract
Prior to European contact, there were no written indigenous languages. Canada’s First Peoples relied on the ‘truth ’ of ancestral oral narratives passed down through thousands of years of observation, knowledge, wisdom and experience. The cultural practices of the Coast Tsimshian people were deeply rooted in our reverent relationship with nature. Place, geographic and tribal names that included clans, crests, sub-crests, wonders and privileges were based on this close relationship. For example, the thoughtful giving of a name reinforced and accelerated each person’s progress toward her/his highest destiny. As a result, at the time of birth, weather patterns, the time of year and the role of the family in tribal life formed the basis for naming. As it is, the widespread use of traditional indigenous names all across Canada is commonplace. Canada itself comes from the Mohawk word, Kanata meaning ‘Community. ’ The central focus of the presentation will be on the indigenous principles of naming that include cultural and spiritual insights, and the historical understanding of the meaning of the name at the time of bestowal. First things first: I am not an Indian! Even after seven hundred years, I and my family do not accept the name “Indian ” as an accurate description of who we are as proud Tsimshian. It is not our fault that Christopher Columbus had a bad sense of direction and completely misunderstood where he was and who it was he was dealing with when he first landed at an island in the Bahamas in 1492. One would think after his fourth trip in 1502–1504, he might have finally gotten it right! Alas, it was not to be. Tsimshian House Each member of royalty and nobility in my tribe, the Tsimshian, belonged to a particular ‘House’ – it was our birthright. The houses reflected our matrilineal society as well as the cultural foundation of our people. For example, a territory belonging to a particular House also included fishing and hunting rights. As well, the family that headed the house, called nobles (later royalty), also owned their stories, ceremonies, names, crests, wonders, privileges and songs. It was considered to be a great honour when a song was given to another house or to a noble from a visiting tribe.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.029 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".