"Respecting the original justice of the claim": reality and legality in John Marshall's epic of Indian divestiture, «Johnson v. M'Intosh»
Bibliographic record
Abstract
This thesis examines Chief Justice of the United States Supreme Court John Marshall's opinion for _Johnson and Graham's Lessee v. William M'Intosh_ (1823) in light of the fictional history he employed in justifying the decision of the Court. I work from Hannah Arendt's conceptions of myth and legend as corrective of history, and conclude in line with Milner S. Ball that the legal transcription of custom into statute finds a natural corollary in the poetic license exercised by forging precedence from obiter dicta. In my examination, I treat law as literature insofar as it allows one to elucidate the elements of _Johnson v. M'Intosh_ that are amorally imperial in nature, and on which America is founded. While legend and law can be formally quite similar, I argue that racist, ethnocentric decisions like _Johnson v. M'Intosh_ demonstrate that if we desire for our laws to endure as the paramount social embodiment of justice, it is essential that the forms of law and legend remain disparate. As I conclude, the violence done Indian tribes by the statutory institution of Marshall's mythical opinion as authoritative, "true" history is unforgiveable and irreparable.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.068 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".