Lessons from the National Indigenous Physical Activity and Wellness (NIPAW) 2019 Conference
Bibliographic record
Abstract
Background: This study investigates the interactions with Indigenous elders, youth, scholars and community members and their shared experiences of Indigenous way of life through the conference and communication through social media. Purpose: This project focused on improving community engagement for the National Indigenous Physical Activity & Wellness (NIPAW) 2019 conference and on how the NIPAW 2019 platform strengthened community participation. Applications of these concepts were explored for future conferences. Methods: Informative videos, promotional posts, and an infographic were made for online promotions of NIPAW 2019 through the Indigenous Physical Activity and Cultural Circle (IPACC) Facebook page and Gmail. Conference logistics and administration were handled through the same platforms. Results: There was a 5.25% engagement rate on the IPACC Facebook page for NIPAW 2019 from the posts representing a 140% increase in engagement over previous years. Communication through the NIPAW Gmail account also allowed for improved planning with the conference speakers. Elder and youth engagement were also facilitated by the fact that the conference was held on Samson Cree reserves near a major high school. Conclusion: NIPAW 2019 effectively decolonized their conference platform through Elder and youth involvement and connection to land. These aspects of the conference improved engagement for Indigenous peoples because they create a safe space for community members to engage in reciprocal, holistic learning. NIPAW 2019 should be used a template for conference proceedings outside of the Indigenous sphere.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".