Race, Power, and Internal Orientalism in the U.S.: Reflections on Edward Said and the Responsibilities of Intellectuals
Bibliographic record
Abstract
Edward Said advocated an activist role for intellectualsand argued for their responsibility to speaktruth to power and to ally with the “weak andunrepresented.” This article examines the ethics ofresponsibility on the part of the intellectual from ageographic perspective. It uses the example ofinternal orientalism in the United States to showthe usefulness of considerations of scale to themoral calculus of the politically engaged intellectual.It begins with a brief review of the issue ofpower within Orientalism, as described by Said,and the responsibility of the intellectual in thatcontext. It then examines these issues in thecontext of internal orientalism in the United States.“The South” is considered as an internal spatialother in the United States, but within this otheringthere are two others, African-Americans and white“Southerners.” The responsibility of the intellectualto each is discussed, and the appropriate stanceof the intellectual on the U.S. Civil War is examinedin this light. The explicit use of scale revealsthe possibility that one may judge the injustice atthe regional scale (slavery) as outweighing anyinjustice created by the power imbalance at thenational scale. In addition, the responsibility of theintellectual to the others of internal orientalisminvolves illuminating the process through whichthe spatial identities are constructed.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.048 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".