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Record W7135528660

Open Air Museums: Representing Ethnography and History, Interacting with Heritage

2020· dissertation· en· W7135528660 on OpenAlexaboutno aff
Hélène Bernardot

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2020
Typedissertation
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsMuseologyEthnographyIndigenousRepresentation (politics)Cultural heritagePerspective (graphical)
DOInot available

Abstract

fetched live from OpenAlex

TITLE Representing History and Ethnography, Interacting with Heritage Analysing Museological Practices at the Huron-Wendat Museum ABSTRACT This master thesis is an analysis of the current specific actions on representation and interaction taken in contemporary ethnographic museums. The aim is to highlight museology pathways used to represent local indigenous culture and to explore how the public is involved with and relates to these specific discourses on heritage. Special attention will be devoted to the study of the shift of museums from authoritative places of education to socially inclusive spaces. The mission of heritage professionals in terms of representation will be analysed, as well as their work on the notions of accessibility and involvement for and with the public. The Huron-Wendat Museum in Wendake, Québec, serves to investigate these museum practices. Drawing from thorough fieldwork and extensive secondary literature, this master thesis will further probe the prevailing notions of identity, continuity and unity of the new museology in a postcolonial context. KEYWORDS ethnographic museums; new museology; cultural heritage; representation; interaction; social inclusion; First Nations; postcolonialism

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)0.004
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 score0.237
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0120.013
Scholarly communication0.0110.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.026
GPT teacher head0.245
Teacher spread0.219 · 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
Published2020
Admission routes1
Has abstractyes

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