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Record W3033723732

Nuevo gerencialismo y educación. Las metáforas del discurso empresarial en blogs para educadores

2019· article· es· W3033723732 on OpenAlexaboutno aff
Amalia Hafner-Táboas, J. Roberto Sánchez-Reina

Bibliographic record

VenueRepositori digital de la UPF (Universitat Pompeu Fabra) · 2019
Typearticle
Languagees
FieldSocial Sciences
TopicEducational theories and practices
Canadian institutionsnot available
Fundersnot available
KeywordsDiscourse analysisCritical discourse analysisSociologyContext (archaeology)HumanitiesPedagogyLinguisticsPoliticsPhilosophyPolitical scienceIdeology
DOInot available

Abstract

fetched live from OpenAlex

Este trabajo propone explorar la interacción entre dos campos discursivos: el del gerencialismo y el de la educación. Partimos de la idea de que, en el contexto actual, las huellas del discurso empresarial pueden ser identificadas en el discurso educativo. En esta investigación examinaremos, desde la perspectiva del Análisis Crítico del Discurso (Fairclough, 2009; Rogers, 2009; van Dijk, 1999), las formas que asume el discurso de educadores que escriben textos públicos. A partir de un corpus de textos recogidos en blogs educativos, identificamos y analizamos las metáforas utilizadas para dar cuenta de la relación entre ambos discursos. Partimos del supuesto de que dichas metáforas se articulan en el marco del discurso nuevo gerencialismo (Alonso y Fernández-Rodríguez, 2013; Boltanski y Chiapello, 2002; Laval y Dardot, 2013) y ponen en evidencia una colonización discursiva del ámbito de la economía al de la educación.

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)0.008
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.009
Science and technology studies0.0050.008
Scholarly communication0.0150.019
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.003

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 head0.007
GPT teacher head0.304
Teacher spread0.297 · 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
Published2019
Admission routes1
Has abstractyes

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