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Record W1551199618 · doi:10.18316/1872

Experiencia intensa e experiencia de imersão: Relatório de observações diretas

2014· article· pt· W1551199618 on OpenAlexaff
Colette Dufresne-Tassé

Bibliographic record

VenueAmericanae (AECID Library) · 2014
Typearticle
Languagept
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

The ways used to provoke the immersion of visitors in museums, whether physical or virtual, have been the subject of several publications over the last fifteen years. Through these forms, we tried to provoke a more involving experience than those raised by previous museographies, called first or second generation. The object of this article is not concerned with these forms, but with the visitor's experience. Considering the limits of this text, I will pay little attention to the works already published on the subject. What differentiates my research from the previous ones - and what justifies the present article - is the fact that my data were collected on the intensity of the visitation experience, while the other studies were based on memories, that is, on what is evoked by memory. of that experience.

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)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.009
Scholarly communication0.0050.004
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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
Published2014
Admission routes1
Has abstractyes

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