Bibliographic record
Abstract
Building on the ground-breaking work on the revitalization of Indigenous laws ongoing over the past decade, this article seeks to contribute to our understanding of how Indigenous languages can be used to recover Indigenous laws. It posits that there is not one single linguistic method, but at least five: 1) the ‘Meta-principle’ method; 2) the ‘Grammar as revealing worldview’ method; 3) the ‘Word-part’ method; 4) the ‘Word-clusters’ method; and 5) the ‘Place names’ method. Using the Mìgmaq language to illustrate, the article explains each method and provides examples of how they can be used to inform Indigenous law revitalization. The article also shows that one does not have to be a fluent, first-language speaker to engage with linguistic methods for Indigenous law revitalization, by highlighting the various published resources like dictionaries and lexicons, reference and teaching texts, atlases, and more, that can be harnessed to engage in this work. This makes engaging with the linguistic methods accessible tothe many Indigenous peoples who, because of the impacts of colonialism, are only starting to re-learn their Indigenous language. This revelation should give greater confidence to the non-fluent that they too can play a role in the revitalization of both their language and laws.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".