Niigaaniiwin
Bibliographic record
Abstract
In 2018, we were awarded a one-year Tri-Council development grant that provided the needed resources and time to work together with community as we developed an application for a larger Indigenous health operating grant. In June 2019, a group of academics, community partners/researchers, and health professionals gathered to create a vision for a community- based health research network. Understanding the importance of community voices and authentic participation, our team hosted a gathering for community members interested in Indigenous health and seeing this proposed health research network become a reality in northern Ontario. As we prepared for our journey towards community-driven research, we asked community gathering participants to add their expertise, voice, and vision to these objectives: Build upon Indigenous health research that is led by communities through funding, connecting, and/or supporting community-based and community-partnered health research. Honour our Indigenous ways of knowing and being so that Indigenous health researchers, students, and organizations can do their work in a good way and feel safe doing so. Support community-based researchers and graduate students with learning opportunities and connect them to mentors. Share knowledge and be able to give back to communities in a way that reflects their needs and not the needs of the college/university. This visioning process brought forth a holistic image of structure, Elders as data keepers, land, language, relationships, youth involvement, the protection of Indigenous knowledges, land-based education, researcher training, addressing power imbalances, mentorship, community involvement, university connections, shifting perspectives, reciprocal sharing, data governance, Indigenous data sovereignty, sharing with respect, directory of experts, and importance of identity. These were all identified by community participants as areas that are important when working within Indigenous health research. Upon the initial announcement of unsuccessful funding, we found ourselves as a network with a responsibility to continue working within community and to further develop the seeds of this Indigenous health network. With the continued guidance of Elders H. Neil Monague and Mary Elliott, we refocused and have brought forth our Indigenous health collective: Niigaaniiwin. This grassroots collective is community-based with support from the academic community. We have gathered the knowledge, wisdom, and stories from the community and are moving forward, hoping to live up to the name gifted to our collective, which means “leading the way.”
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.039 |
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".