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Record W3044979669 · doi:10.5377/rlpc.v1i2.9837

EL LUGAR E O TEMPO DA DIVERSIDADE CULTURAL NO CURRÍCULO

2020· article· pt· W3044979669 on OpenAlexfundno aff
Isabel Carvalho Viana

Bibliographic record

VenueRevista Latinoamericana Estudios de la Paz y el Conflicto · 2020
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsnot available
FundersEuropean Regional Development FundFundação para a Ciência e a TecnologiaUniversidade do MinhoInternational Council for Canadian Studies
KeywordsSociologyHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

This article, based on debates and guidelines of cultural diversity and development raised by Agenda 2030, which projects it across all objectives, being assumed by UNESCO as of global importance, looking at it as necessary for sustainable development as much as biodiversity, seeks to problematize its relevance in the curricula, as an integral place of valuable knowledge. We start from the belief that cultural diversity is the reality of the dialogue for the development and affirmation of the exercise of human rights. It is a natural resource of human regeneration to creatively emancipate the development of humanity, constituting one of the greatest challenges for the curricula of the different educational systems in the world. From a holistic approach, we seek to interpret the place and time of cultural diversity in the curriculum. It is hoped that this article will contribute to highlighting the importance of cultural diversity and assuring visibility in curricula, an authentic way to respond to the commitment that the 2030 Agenda represents for inclusive human development, as a shared and plural creative citizen responsibility, to affirm harmonious development of humanity.

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)0.009
metaresearch head score (Gemma)0.017
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0090.015
Scholarly communication0.0220.011
Open science0.0020.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.003

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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
Published2020
Admission routes1
Has abstractyes

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