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Record W3135062683 · doi:10.5539/jel.v10n2p109

States, Institutions, and Literacy Rates in Early-Modern Western Europe

2021· article· en· W3135062683 on OpenAlexvenueno aff
Tyrel C. Eskelson

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyIncentiveNumeracyPopulationEnlightenmentPer capitaPoliticsEconomic growthPolitical scienceEconomicsSociologyMarket economyDemography

Abstract

fetched live from OpenAlex

The purpose of the paper is to develop the theory that structural or procedural changes in institutions precede changes in education in a society. It examines the development of pre-modern institutions in Western Europe in the 16th and 17th centuries and the influences this had on growth in literacy rates within these states. Literacy rates in Western European countries during the Middle Ages were below twenty percent of the population. For most countries, literacy rates did not experience significant increases until the Enlightenment and industrialization. Two early exceptions to this broad trend were the Netherlands and England, which had achieved literacy rates above fifty percent of their populations by the mid-seventeenth century. The explanations for these divergent trends are the structural changes in formal institutions that embodied capital markets, protected private property, and overall established the initial steps in developing modern political institutions. This created incentives to invest more in schools per capita as well as incentives for a middle class to invest more in literacy and numeracy skills for a market-exchange economy that was becoming more specialized in division of labor.

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 score0.578
Threshold uncertainty score0.188

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 head0.036
GPT teacher head0.284
Teacher spread0.249 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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