Converging Methods and Tools: A Métis Group Model Building Project on Tuberculosis.
Bibliographic record
Abstract
Indigenous (Métis, First Nation, and Inuit) peoples and communities in Canada, especially in the prairies, continue to experience disproportionate levels of tuberculosis (TB) compared to the rest of the Canadian born population. This inequitable distribution of TB disease burden demands effective policy, program, and practice responses. These have so far failed to materialize, perhaps in part because of limitations in the approaches we have taken to understanding the issue. As well, these responses have largely been grounded in western scientific paradigms. Science is the search and the re-search for knowledge and this varies according to the perspectives and paradigms of the researcher(s) and stakeholders. In this project, the student researcher collaborated with the Métis Nation-Saskatchewan (MN-S) and two volunteer health researchers to adapt and ground a western paradigm and methodology (System Dynamics and Group Model Building) to a Métis research paradigm to understand experiences of tuberculosis (TB) among Métis people. Data collection took place in a 2-day Métis-adapted group model building (GMB) workshop. The outcome is a causal loop diagram with associated stories co-created by the team and the workshop participants. The workshop was evaluated using a storytelling and story listening method that explored the appropriateness of adapting GMB within a Métis research context. The approach was determined to be successful methodologically, and substantively new knowledge was created in our Métis community about the determinants of TB. This research was a journey of diversity, working at the intersection of knowledge systems to produce a new understanding of a health issue as complex as TB.
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 | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".