Bibliographic record
Abstract
This paper is about the place of Indigenous people in an early instance of a culture war in the United States: the conflict in the 1970s over an innovative middle-grades social studies curriculum called “Man: A Course of Study” (MACOS). Funded by the National Science Foundation, MACOS sought to revamp social studies education by addressing big questions about humans as a species and as social animals. It quickly came under fire from conservatives and helped to solidify the concept of “secular humanism” as a social threat. A broad conservative organizing effort, whose effects can still be felt today, eventually ended not only MACOS, but the very viability of school curriculum reform projects on the national level. Though this story is familiar to historians of American education, this paper argues for its centrality to the development of contemporary conservative politics and the early history of the culture wars. It also takes up the largely unaddressed issue of how Indigenous people figured in the MACOS curriculum and in the ensuing controversy. Focusing on the ethnographic film series featuring Netsilik Inuit that was at the heart of the MACOS curriculum, this paper addresses the largely unacknowledged legacy of Indigenous pedagogy, to argue that the culture war that led to the demise of the MACOS project also represented a lost opportunity for Indigenous knowledge and teaching to be incorporated into the formal schooling of American children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".