Indigenous linguistic vitality and health: A scoping review protocol
Bibliographic record
Abstract
Objective: The objective of this scoping review is to document and assess the extent and scope of literature on the relationships between Indigenous linguistic vitality/revitalization and health in Indigenous populations in anglophone, settler-colonial societies. Introduction: Indigenous communities have long known about the importance of their languages for wellbeing, but this topic has only recently received attention in research and policy. As an emerging, heterogeneous and interdisciplinary field of work that has not yet been comprehensively reviewed, this scoping review will fill important gaps and guide public health and policy recommendations. Inclusion criteria: Academic and grey literature written in English will be included that describes a connection between Indigenous linguistic vitality/revitalization and health/wellness in one of the four anglophone, settler-colonial states: Canada, the United States of America, New Zealand, or Australia. Methods: The proposed scoping review will be conducted in accordance with the JBI methodology for scoping reviews (Peters et al., 2020). Databases to be searched include MEDLINE (Ovid), Bibliography of Native North Americans (EBSCO), Australian Education Index (ProQuest), and Linguistics and Language Behavior Abstracts (LLBA: ProQuest). Sources of unpublished studies/grey literature to be searched include desLibris, PsycEXTRA, Native Health Database, iPortal, and Google Scholar. Results will be presented in charts, tables and narrative formats.
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 | 0.167 | 0.114 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.016 |
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".