Archives as Good Medicine: Rediscovering Our Ancestors and Understanding the Root Causes of Intergenerational Trauma
Bibliographic record
Abstract
My field research was directed towards tracing back my genealogical history to comprehend the social ills that plague contemporary Cree and Metis communities in Saskatchewan. Moreover, I undertook this research to better understand why I, a reformed addict and homeless person, along with the rest of my biological family, was so troubled in the modern times and why we had so many negative social barriers and problems. Ultimately, however, this paper is a very preliminary work and is incomplete. In the future, I hope to publish a more-detailed body of work in an extended thesis which, I am hoping, will help other Metis and First Nations people understand, combat, and cope with intergenerational trauma. I am basically trying to build a template for Indigenous wellness through historical research. In peeling back the layers of the Metis historical onion, I found that we “Michif” are a nation that both predates Canada as well as suffers from the mechanical processes of colonialism that helped create it.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".