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Record W4327522025 · doi:10.21501/2744838x.4496

La ética como saber, encuentro y reconocimiento con el Otro

2022· article· es· W4327522025 on OpenAlexaff
Jesús Darío Pérez Sierra, Adriana Zapata Arcila, Mateo Vásquez Grajales, Andrés Eduardo Gómez Lopera, Diana Lucía Madrigal Torres

Bibliographic record

VenueCiencia y Academia · 2022
Typearticle
Languagees
FieldAgricultural and Biological Sciences
TopicCultural and Social Dynamics
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Las sociedades contemporáneas evidencian una falta de formación para la práctica de la ética, lo que se ve reflejado en los graves problemas y crisis que vivimos, entre los cuales se destacan la violencia, el fanatismo y la exclusión. Este texto se desarrolla en virtud de resignificar el papel de la ética como un saber necesario, con el fin de fomentar una cultura para el reconocimiento y el encuentro con el otro. Se ha enfocado en una pregunta: ¿por qué la ética nos permite una experiencia del encuentro y el reconocimiento del otro como una persona humana que tiene dignidad, que merece respeto y cuidado? Para su tratamiento se ha seguido el método hermenéutico, con un carácter dialógico, crítico y reflexivo. En cuanto a los referentes teóricos figuran Adela Cortina y otros filósofos tan reconocidos como son Emmanuel Lévinas y Byung-Chul Han. En suma, se trata de concebir la ética como un saber fundamental que nos sirve para la construcción y reconstrucción del tejido social, todo con la intención de que sea un recurso que aporte a la transformación de nuestras realidades sociales y culturales.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0130.043
Scholarly communication0.0140.006
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0090.002

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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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 designTheoretical or conceptual
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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