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Record W4403673018 · doi:10.17118/11143/21998

Construir la escuela, definir una lengua : posicionamientos en relación con la(s) lengua(s) de enseñanza en colonias de migrantes europeos en Argentina (1860-1870)

2024· article· es· W4403673018 on OpenAlexvenueno aff
Mónica Baretta

Bibliographic record

VenueCircula · 2024
Typearticle
Languagees
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Resumen : El presente artículo se ocupa de indagar en el problema de la selección de una(s) lengua( s) de enseñanza escolar en un espacio singularmente diverso: las colonias agrícolas formadas en la región rural de la provincia de Santa Fe (Argentina) desde mediados del siglo XIX, a partir del aporte inmigratorio europeo. Específicamente, se analizará el modo en que las primeras dos colonias establecidas en esa región, Esperanza y San Carlos, habitadas mayoritariamente por familias de habla francesa y alemana, discutieron el modo en que debía organizarse la institución escuela, fundamentalmente en relación con la(s) lengua(s) de enseñanza. Educar (o no) a los niños en un espacio cultural y lingüísticamente diverso constituyó una verdadera polémica caracterizada por posicionamientos encontrados. A partir del análisis discursivo de un corpus constituido por correspondencia y prensa periódica, se señalará cómo funcionarios y familias con diferentes trayectorias culturales, políticas y educativas asignaron distintas valoraciones a la escolaridad en general, y a la alfabetización en particular.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes1
Has abstractyes

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