Bibliographic record
Abstract
Acknowledging the occurrence of family violence in our community is an integral step along the Sḵwx̱wú7mesh pathway toward healing and wellness for our families and community members. Within this work, it has been important for us to give cultural context to what we mean by “family violence”, better held in the language as kwétsiwit na wa ḵeyátnewas—violence toward one another. This short film was created as an offering of space for this context, while upholding our Sḵwx̱wú7mesh traditions of oral storytelling as a generative knowledge practice during the Sḵwx̱wú7mesh Úxwumixw Family Wellness Week in November 2022. This short film honours the ancestral wisdom of Sḵwx̱wú7mesh Elders and Knowledge Keepers as they share stories of traditions, teachings of love, connection, and hope. For us, it has allowed us to create safe space for both truth and hard conversations in our understandings of kwétsiwit na wa ḵeyátnewas. The stories within highlight the resilience of Sḵwx̱wú7mesh families to remember where we came and how we are moving forward on a pathway to healing. Community should be a place where people & families feel safe & secure. Restoring dignity, redressing harms & learning from the past as we see each other with renewed understanding in the need to move forward. Sḵwx̱wú7mesh cultural practices and traditions serve many purposes - often these traditions not only help define our community; they help create community. We are Sḵwx̱wú7mesh strong. While rooted in the Sḵwx̱wú7mesh context, we know that there are echoes of this story and healing path in First Nations communities across the country. It is for this reason it feels important to share. This work was created within community, guided by the wisdom of Yataltenat, Kelley McReynolds— Sḵwx̱wú7mesh Nation member and Director of Ayás Mén̓men. Hannah Rushton—Team Leader of the Ayás Chap Program & Wellness Team; and Calder Cheverie, filmmaker and (former) Team Leader of Youth Services are invited guests into this community, both honored and grateful to be helping relatives. Sḵwx̱wú7mesh Úxwumixw is located in the Lower Mainland region of British Columbia with traditional territory extending from Vancouver to Gibsons Landing, and north of the town of Squamish into the Elaho and Squamish Valleys. We hold our hands up to the knowledge keepers for their stories & teachings in this video.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.222 | 0.087 |
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".