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Record W4281898678 · doi:10.1201/9780429299070-62

The impact of mentality and emotions in the creation of the magazine Claridade

2022· book-chapter· en· W4281898678 on OpenAlexfundno aff
Hilarino da Luz

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicPsychology and Mental Health
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaFederation for the Humanities and Social Sciences
KeywordsCape verdeViewpointsPower (physics)ArchipelagoEpistemologyAestheticsSociologyPsychologyHistoryPhilosophyArtVisual artsEthnology

Abstract

fetched live from OpenAlex

This chapter addresses the mentality and the emotions of the various individuals who created the magazine Claridade (1936). It considers how the creative power of the magazine’s founders and contributors was based on these two elements (mentality and emotions) and their observations of the Cape Verdean reality. We will consider two types of emotions – positive and negative – as described by Caruso Samel and references to other authors who approach this topic from a spiritual point of view, mainly that of Christian Rationalism, a philosophy that incorporates scientific elements).We will also show how human beings can form their ideas by exerting their own free will and reach the areas where Total, Absolute, and Universal Intelligence are. This concept is the founding principle of the magazine and the basis of the viewpoints of authors such as Onésimo Silveira. In short, this study confirms that the creation of the magazine Claridade was based on the two aforementioned types of emotions and its authors’ penchant for observation. These facts, influenced by free will, made this group feel that it was imperative to divulge the nature of the Cape Verde archipelago and Cape Verdean literature.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.398
Teacher spread0.354 · 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
Published2022
Admission routes1
Has abstractyes

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