Nature connections: Cultural heritage, identity and wellbeing in Vancouver, Canada
Bibliographic record
Abstract
Abstract I came to Vancouver, Canada in March 2014 to give a paper on ‘The Cultural Value of the Arts for Health and Well-Being’ at the 4th International Conference on Health, Wellness & Society: Holistic Health 1 at British Columbia University. I knew absolutely nothing about the First Nations people, their arts, culture and history, and how this relates to a Canadian identity. What I know now is that the cultural identity of many of these peoples is linked to their language, and that of the remaining thirty-two indigenous languages in British Columbia, some are near extinction. For these peoples, their language is their way of being and who they are. It is encouraging to learn that projects which promote language revitalization create the opportunity for cultural well-being, through the cultural connections via the traditional values embedded within each indigenous language. In the same way that we know that Western medicine treats the body, not the person, the First Nations peoples’ loss of language is holistically integrated into the whole of their history of colonization, beliefs, medicine, spiritual and cultural practices. Their cultural heritage is interwo-ven to their wellbeing.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".