A great rattling of dry bones: The emergence of national standards in the early 20th century
Bibliographic record
Abstract
The founding of the MLA in 1883 signaled a victory for modern languages in their struggle to gain academic recognition. Greek and Latin were dealt yet another blow to their prestige when “modern language men” persuasively argued that French and German had the same virtues that the classicists had arrogated to themselves, namely a rich literature, efficacy in mental discipline, and an aid to mastering other disciplines. Indeed, the modern languages could go one step further, claiming their practical value in contemporary society. At the same time, waves of immigration were bringing about increased growth in high school enrollments and a more diverse student body, causing concern among many educators. At the 1891 meeting of the National Council of Education in Toronto, the chair of the Committee on Secondary Education, James H. Baker, complained that “the present condition of affairs [as regards high school curricula] is chaotic and that it may be improved in many respects” (Baker, cited in “Report of the committee of 10” School Journal, 1895, p. 718). Such was the historical moment that motivated a series of reports evaluating the place of modern languages in the curriculum, the best ways to teach them, and above all, standards of achievement for entrance into college. In this chapter, I will examine two of these reports—the Report of the Committee of Ten and the Report of the Committee of Twelve—as well as the formation of the College Board. I will emphasize their importance for standard setting, articulation, and assessment in the context of the educational culture of the times.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".