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Record W2897679041 · doi:10.21696/rcsl9162018715

Análisis histórico-crítico del discurso de la política pública de las pesquerías de abulón en la península de Baja California, México (periodo analizado 1947-1993)

2018· article· es· W2897679041 on OpenAlexaff
Magdalena Lagunas-Vazques, Luis Felipe Beltrán‐Morales, Alfredo Ortega‐Rubio

Bibliographic record

VenueRevista de El Colegio de San Luis · 2018
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsCanadian Bank Note Company (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

En este artículo se presenta un análisis histórico-crítico de un periodo de 80 años enfocado en el discurso sobre las regulaciones de la pesquería de abulón en México, cuyo objetivo es describir esta actividad comercial y analizarla en un contexto amplio. Para ello, se realiza una revisión de bibliografía especializada y de instrumentos jurídico-normativos; asimismo, se emplea el análisis crítico del discurso y la historia conceptual. Entre los resultados obtenidos, destaca la notoria separación del discurso político en dos periodos, así como la identificación de las transformaciones y de la legitimación de un discurso que desplaza al sector en este periodo. Una de las limitaciones de la investigación fue la falta de datos empíricos desde los actores principales, es decir, los pescadores. La originalidad del estudio se centra en que no existía a la fecha una investigación sobre el análisis crítico del discurso de la pesquería de abulón, pese a que es una pesquería importante socioeconómica y ambientalmente en México. Finalmente, como hallazgo, se puede afirmar que, en la conceptualización histórica, el corpus se puede dividir en dos periodos, y se identificaron transformaciones paulatinas y la legitimación del discurso.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.253
Teacher spread0.243 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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