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Christianity and Education Law

2023· book-chapter· en· W4388826776 on OpenAlexaboutno aff
Charles L. Glenn, Jan De Groof

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPolitical sciencePluralism (philosophy)PoliticsCompromiseChristianityLawRivalryReligious pluralismPublic administrationSociologyHistory

Abstract

fetched live from OpenAlex

Abstract Education, shaping the character and the convictions of the young, has always been a concern of Christians, and church leaders have sought through exhortation and through creating schools to ensure its provision. With the Reformation, this became an urgent task because of a focus, among Protestants, on the ability to read the Bible and a flood of publications by Luther, Calvin, and other leaders. There was a corresponding concern by Catholic leaders, in areas of religious rivalry, to solidify their support, leading to the formation of religious teaching orders. The primary focus of this chapter, however, is upon the last two hundred years, when governments, through laws and policies, have used schooling for nation-building and when the creation of loyal citizens either fostered or conflicted with the Christian mission and character of schools. In such cases as Germany, the United Kingdom, Canada, and Scandinavia the respective educational goals of governments and churches involved generally fruitful collaboration. Elsewhere, such as the Netherlands, Belgium, Mexico, and Spain, periods of intense conflict were followed by compromise. In yet other countries, such as France and the United States, deeply ideological differences over schooling led to continuing political and legal conflicts and recent efforts to find ground for principled pluralism in education. While this chapter cannot discuss the details of how law in these and other countries has addressed the competing educational goals of governments and churches, it presents key examples and principles for consideration.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.876
Threshold uncertainty score0.705

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 studies0.0010.000
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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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