áanzho ha’shi ‘dał’k’ida’, ‘áá’áná’, ‘doo maanaashni’: Welcoming ‘long ago’, ‘way back’ and ‘remember’—as an Ndé decolonization and land recovery process
Bibliographic record
Abstract
Memory, re-membering, oral history and critical recovery are pillars of a Ndéi decolonization and self-determination process in indigenous peoples’ rancherías along the Lower Rio Grande River and Texas-Mexico border. The goals of this process are to dismantle the U.S. border wall bifurcating the customary lands of indigenous peoples; to reclaim dispossessed lands; and to revitalize Ndé ways of life in autonomy and self-governance. In the Ndé language, we can communicate in this way about a recovery process, Dáanzho ha’shi ‘dał’k’ida’ áá’áná ‘doo maanaashni’—‘long ago, way back’ and gain knowledge, insights and tools from our foremothers’ and forefathers’ struggles and resistance strategies. In October 2009, when the U.S. violently coerced and forced vulnerable peoples off the community lands along the Texas-Mexico border, and obstructed international covenants, treaties, and human rights laws, the discursive legal fiction of ‘terrorism’ was deployed to launch a massive land grab. At this stage, however, indigenous peoples along the Lower Rio Grande River were already involved in an ongoing process of restoring and implementing Ndé law and governance systems based upon women’s traditional knowledge and historical experiences in land defense.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".