Book Review: The Rediscovered Self: Indigenous Identity and Cultural Justice, by Ronald Niezen
Bibliographic record
Abstract
A review of The Rediscovered Self: Indigenous Identity and Cultural Justice, by Ronald Niezen.In the suite of essays comprising The Rediscovered Self, Ronald Niezen, professor of anthropology at Canada’s McGill University, offers an intriguing, multi-layered conception of the development of indigenous identity and the associated development of indigenous rights over the past two decades or so. On his account of things, the contemporary struggle for indigenous rights is a key component of a broader struggle to refine, communicate and defend the very identity of indigenous peoples as distinct social entities in the face of an increasingly cosmopolitan and homogenising world order. For Niezen, this broader struggle plays out most interestingly through its strategic engagement with the discourse and practice of national and international law, through its use of cutting edge modes of communications technology, and through its transnational collaborative orientation. The indigenous peoples of the world are forging a new conception of themselves - they are rediscovering themselves, in Niezen’s terms - and are achieving significant political success through their creative use of contemporary discourses, technologies and institutions.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".