Knowledge to action: First nations engagement with research for community benefit.
Bibliographic record
Abstract
Knowledge translation is the sharing of knowledge in an effort to make research more meaningful to society.Currently, many gaps exist in effective knowledge to action especially for research carried out with Aboriginal peoples.My master's research explores knowledge translation and knowledge to action in regards to recent research initiatives in, and for, the Takla Lake First Nation (TLFN).Using content analysis based on focus group interviews with 17 community participants, I was interested to see if community members' expectations of the research process had been met, and to hear from community members themselves about strategies and approaches they wanted taken to translate knowledge obtained from research into actions.This thesis research finds that a better understanding of the context and ways of knowing of a group is necessary to undertake effective research and knowledge translation activities.Also, there is a need for more defined and established evaluation criteria and techniques for Aboriginal knowledge translation.Finally, I argue that the TLFN want future actions in the community to derive from strength-based approaches based in the traditional TLFN culture as a method to improve community unity, health and 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 | 0.090 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".