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Visitors’ Perception of Maritime Museums: Preference Analysis

2022· article· en· W4304207178 on OpenAlexaboutno aff
Ivan R. Nikolaev

Bibliographic record

VenueObservatory of Culture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVisitor patternExhibitionTourismPerceptionCultural heritageWork (physics)Quarter (Canadian coin)Exposition (narrative)PreferenceSample (material)GeographyPublic relationsSociologyPolitical sciencePsychologyArchaeologyEngineeringArt

Abstract

fetched live from OpenAlex

The article considers the results of a sociological survey devoted to the analysis of preferences and perception of maritime museums by their potential visitors. The topic of maritime heritage and its museification is relevant due to a number of strategic documents devoted to the development of activities aimed at marine resources and affecting the preservation and actualization of maritime heritage. Although there are studies devoted to maritime museums in Russia and abroad, the attitude of visitors to this kind of expositions has not been studied before. This lacuna, along with the need to develop maritime museums, determined the purpose of this work — to analyze the visitor’s perception of the topics presented in the expositions of maritime museums. The study is based on a sociological survey (20 topics were presented in the questionnaire). The sample size was 500 people, including 171 men and 329 women. The average age of the respondents was 32 (±11) years in the range from 12 to 74 years. The majority of the respondents live in the Central and North-Western Federal Districts. Slightly more than a quarter of the respondents work in museums or are associated with cultural and tourism institutions. The article analyzes the estimated results in accordance with the respondents’ age, gender, as well as their involvement in museum activities. The results of the research can be applied within the framework of scientific and exposition activities – in particular, when creating scientific concepts for new expositions and exhibitions dedicated to maritime heritage. In addition, they can be used in preparation of training courses dedicated to the study, preservation and museification of maritime heritage.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.045
GPT teacher head0.303
Teacher spread0.257 · 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 designObservational
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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