Review: <i>Making Indian Law: The Hualapai Land Case and the Birth of Ethnohistory</i>, by Christian W. McMillen
Bibliographic record
Abstract
Book Review| November 01 2008 Review: Making Indian Law: The Hualapai Land Case and the Birth of Ethnohistory, by Christian W. McMillen Making Indian Law: The Hualapai Land Case and the Birth of Ethnohistory. By Christian W. McMillen. (New Haven, Conn., Yale University Press, 2007. xx + 284 pp. $38) Arthur J. Ray Arthur J. Ray University of British Columbia Search for other works by this author on: This Site PubMed Google Scholar Pacific Historical Review (2008) 77 (4): 668–670. https://doi.org/10.1525/phr.2008.77.4.668 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Arthur J. Ray; Review: Making Indian Law: The Hualapai Land Case and the Birth of Ethnohistory, by Christian W. McMillen. Pacific Historical Review 1 November 2008; 77 (4): 668–670. doi: https://doi.org/10.1525/phr.2008.77.4.668 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentPacific Historical Review Search This content is only available via PDF. © 2008 by the Regents of the University of California2008 Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".