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Record W7038312176

A great rattling of dry bones: The emergence of national standards in the early 20th century

2009· article· en· W7038312176 on OpenAlexaboutno aff

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2009
Typearticle
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsGloomGovernment (linguistics)Context (archaeology)Subject (documents)GermanExposition (narrative)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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