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Record W4239068153 · doi:10.3138/cras.39.2.139

Tonto on Vacation, or How to Be an Indian Lawyer

2009· article· en· W4239068153 on OpenAlexvenueno aff
Chadwick Allen

Bibliographic record

VenueCanadian Review of American Studies · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsComicsIndigenousFantasyHistoryWhite (mutation)CommissionMedia studiesLiteratureLawSociologyArtPolitical scienceChemistry

Abstract

fetched live from OpenAlex

This essay investigates the Dell Comics series titled The Lone Ranger's Companion Tonto, produced between 1951 and 1959. In stark contrast to the other versions of this popular Indian character that appeared at one time or another in various media, the Dell comic books depict a Tonto who operates on his own, without the white Ranger at his side. These 1950s comic books, I argue, reflect the dominant culture's fantasy about the purpose of the newly formed Indian Claims Commission (ICC), created in 1946 as a “final solution” to the so-called “Indian problem.” In this public fantasy, Tonto is made to play the role of the “Indian lawyer”: a strategic go-between who ostensibly works on behalf of Indian tribes but who actually promotes the expropriation of Indigenous lands and the assimilation of Indigenous nations.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.007
Scholarly communication0.0070.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.004

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 head0.050
GPT teacher head0.374
Teacher spread0.324 · 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 designQualitative
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

Citations2
Published2009
Admission routes1
Has abstractyes

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