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Record W2883228411 · doi:10.2478/bjals-2019-0014

“Felix Cohen Was the Blackstone of Federal Indian Law:” Taking the Comparison Seriously

2019· article· en· W2883228411 on OpenAlexaff
Adrien Habermacher

Bibliographic record

VenueBritish Journal of American Legal Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsAppealJurisprudenceLawScholarshipPoliticsLegal historyCivil servantLegal educationCommon lawPolitical scienceServantSociologyEngineering

Abstract

fetched live from OpenAlex

Abstract This paper explores the many facets of Rennard Strickland’s comparison between Sir William Blackstone, author of the 1765–69 Commentaries on the Laws of England, and Felix Cohen, architect of the 1942 Handbook of Federal Indian Law. It consists of a side by side analysis of both authors’ master works, political and educational projects, as well as general contribution to jurisprudence. It reveals that despite the stark differences between Blackstone’s work on the English common law from his professorship at Oxford in the late eighteenth century, and Cohen’s endeavors on the US federal law concerning Native Americans as a civil servant at the turn of the 1940s, there are remarkable similarities in the enterprises of legal scholarship the two jurists took on, the larger political projects they promoted, and their role in the development of legal thought. The idea that “Felix Cohen was the Blackstone of Federal Indian Law” has stylistic appeal and could have been little more than a gracious way to celebrate Cohen. An in-depth comparative examination of legal history and jurisprudence however corroborates and amplifies the soundness of the comparison.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.021
Scholarly communication0.0080.007
Open science0.0010.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.026
GPT teacher head0.326
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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