Old Knowledge, New Tools: Applying an Indigenous Approach to Social Network Analysis
Bibliographic record
Abstract
Program work with American Indian and Alaska Native (AI/AN) communities necessitates Indigenous approaches and methods for evaluation. AI/AN researchers are working to reclaim evaluation as a traditional value and identify methods that fit into existing Indigenous evaluation frameworks. However, an increased understanding of how to utilize data collection tools appropriately and how they fit within these Indigenous frameworks is still needed. In this article, the author describes the process, rationale, and reflections on using a social network analysis tool while grounded in Indigenous evaluation principles. We discuss how displaying the results using a GIS story map can tell the story of a community of practice of Indigenous plants and foods educators. This article addresses the Southern Door—Be of Good Mind—as it describes a method that centres on community, honors relationships, and focuses on resiliency. By presenting the results through a GIS story map, the data can be gifted back to the communities and connect the relationships on a spatial scale to honour the inseparable connections between Indigenous plants and foods work and the land on which it takes place.
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 | 0.056 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".