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Record W2793090024 · doi:10.14201/hedu2016353552

Artesanías, oficios y recuperación rural: las Hermanas de la Caridad, Halifax, y la formación profesional en la Bahía de Terence, Nueva Escocia, 1938-1942

2016· article· es· W2793090024 on OpenAlexafffundabout
Sasha Mullally, Heidi MacDonald

Bibliographic record

VenueHistoria de la Educación · 2016
Typearticle
Languagees
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of LethbridgeUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNova scotiaBayVocational educationPovertySociologyCapitalismHumanitiesPolitical scienceHistoryArtEthnologyArchaeologyPedagogyLawPolitics

Abstract

fetched live from OpenAlex

En respuesta a la pobreza rural asociada al declive de la pesca, el ascenso del capitalismo industrial y el impacto de la Gran Depresión, las Hermanas de la Caridad pusieron en marcha un programa de formación profesional de tejido y carpintería en la pequeña comunidad de Terence Bay, Nueva Escocia, en 1938. El senador William Dennis, un defensor del Movimiento de Nueva Democracia (New Democracy Movement) financió el programa. Debido a que las hermanas basaron sus reclamaciones en el éxito que observaron en los cambios de conducta entre los residentes de Terence Bay, el programa puede ser percibido como un ejemplo de terapia liberal en materia educativa. Un modelo que enfatiza el logro de objetivos sociales en vez de la transferencia de habilidades y capacidades diferenciadas para los alumnos/as. Este artículo, que se centra en los años 1938-1943, señala los esfuerzos de rehabilitación en Terence Bay, describe los programas que implementaron las hermanas y evalúa las definiciones de éxito atribuidas a su escuela de formación sólo unos pocos años más tarde.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.568
Threshold uncertainty score0.860

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
Scholarly communication0.0020.001
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 head0.005
GPT teacher head0.273
Teacher spread0.268 · 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

Citations1
Published2016
Admission routes3
Has abstractyes

Explore more

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