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A CONCEPÇÃO DE “DISTÂNCIA” EM JACQUES HENRIOT: UM ESPAÇO VIRTUAL DE JOGO

2021· article· pt· W3212044178 on OpenAlexaff
Maude Bonenfant

Bibliographic record

VenueBrazilian Journal of Policy and Development · 2021
Typearticle
Languagept
FieldPsychology
TopicPsychology and Mental Health
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsDialecticGRASPCore (optical fiber)MathematicsComputer sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Jacques Henriot asserts that the game refers to one and the same idea. He believes in a semantic core, a common element, a “unity of the game” among the diversity of games. This “common denominator”, as Caillois puts it, is what makes all types of games playable. This semantic core is, according to our reading of Henriot, the “distance” that defines the function of play, for the idea of play must first be understood in a mechanical way. Play is above all that emptiness which allows us to say that there is play: in the parts of a gear, in the joints, in a hinge, etc. Playing is then considered as a dialectical operation that puts the player at a distance. The form of the game is this distance, this interval that allows the game to exist for a player. The distance is thus a key concept to understand Henriot’s conception of the game, but instead of approaching the distance from a psychoanalytical point of view, we propose to define this concept starting from the concept of “virtuality” in order to fully grasp the richness of Henriot’s thinking.

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)0.005
metaresearch head score (Gemma)0.010
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 score0.014
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.025
Scholarly communication0.0080.009
Open science0.0010.007
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.379
Teacher spread0.348 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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