Because This Land is Who We Are
Bibliographic record
Abstract
<JATS1:p>Because This Land Is Who We Are is an exploration of environmental repossession, told through a collaborative case study approach, and engaging with Indigenous communities in Canada (Anishinaabe), Hawai'i (Kanaka Maoli) and Aotearoa (Maori). The co-authors are all Indigenous scholars, community leaders and activists who are actively engaged in the movements underway in these locations, and able to describe the unique and common strategies of repossession practices taking place in each community.</JATS1:p> <JATS1:p>This book celebrates Indigenous ways of knowing, relating to and honouring the land, and the authors' contributions emphasize the efforts taking place in their own Indigenous land. Through engagement with these varying cultural imperatives, the wider goal of Because This Land Is Who We Are is to broaden both theoretical and applied concepts of environmental repossession, and to empower any Indigenous community around the world which is struggling to assert its rights to land.</JATS1:p>
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".