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Teachers’ Practices and Teacher Models: Anísio Teixeira and Initial Teacher Training (Rio de Janeiro, 1932–1935)

2022· article· en· W4311949488 on OpenAlexvenueno aff
Diana Gonçalves Vidal

Bibliographic record

VenueEncounters in Theory and History of Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Education Research in Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationNarrativeTeacher educationOral historyPedagogyWork (physics)Mathematics educationTraining (meteorology)Teacher preparationSociologyPsychologyComputer scienceEngineeringArtLiteratureGeography

Abstract

fetched live from OpenAlex

To investigate the relationship between teachers’ practices and teacher models, this article takes as its main thread the work of Anísio Teixeira at the Teachers School of the Institute of Education, in Rio de Janeiro, between 1932 and 1935. In doing that, it resorts to oral and written sources and dares to outline a research methodology. The narrative is organized into four parts, and an introduction. The first part offers a general description of the place where Teixeira’s professional work is developed. Only those aspects of the history of the creation of the Institute of Education deemed as necessary to the study are described. In the second part, we explore the broad features of that teacher practice, interweaving written and oral documentation. In the third part, we focus on the issue of the sources. As our final comments, we consider the importance of historical investigations on teachers’ practice and models. Keywords: history of education, teacher training; oral history; teachers’ lives; teacher models

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.014
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.078
GPT teacher head0.327
Teacher spread0.248 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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