Alcohol is a great destroyer: A call for insight on ceremonial approaches for coping with FASD
Bibliographic record
Abstract
As a seasoned community helper, I worry about the generation now assuming roles as healers, leaders and warriors, and continuing the fight for fundamental change in the relationship between Canada’s Indigenous peoples and those privileged to inherit colonial legacies of European global colonization. I now view my personal journey of self discovery as an unending marathon. I honour “Runners” like Tom Longboat who represented the strength and vitality of healthy and sober communities. Traditional runners were as dependent on path-finders as we are today, yet they travelled with dedication and carried important information that sustained community integrity. Open discussion about the devastation of FASD is the most important conversation required across our territories today. We need to prepare good messages and good minds for the next generation to bring forward. This self-reflective paper seeks solace in rituals such as the Haudenasaunee Reqickening Addresses to allow those who suffer to “stand again in front of the people.”
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.019 |
| 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".