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Record W2804767908 · doi:10.1080/13527258.2018.1476398

‘Let’s find out’: the historian laureate and participatory heritage discovery

2018· article· en· W2804767908 on OpenAlexaffabout
Karen Wall

Bibliographic record

VenueInternational Journal of Heritage Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCitizen journalismNobel laureateCultural heritageHistorySociologyEnvironmental ethicsArt historyArchaeologyPolitical scienceArtPhilosophyLiteratureLaw

Abstract

fetched live from OpenAlex

Developing modes of engagement in the construction of public heritage knowledge emphasize participatory media and active collaboration with citizens. The City of Edmonton created the first historian laureate position in Canada in 2010, and related programs are still rare. This article considers the interaction of narrative content with social and technological contexts of production, viewing the role of the historian laureate as amateur historian and professional storyteller. The historian laureate operates primarily in accessible contexts of leisure, mediated in part through digital technologies, and can respond relatively directly to community interests as a heritage coordinator rather than expert. Rather than representing oppositional or disruptive power to official heritage discourses, the project enables the production of ‘small heritages’ through a series of story episodes. These stories focus on events, people, places and artifacts that typically fall outside the meta-narratives and monuments of a city’s heritage landscape. The historian laureate, embodying or articulating local experience in ways amenable to leisure activity, demonstrates capacities to produce largely indeterminate, diverse and porous ideas of place and histories as part of a bottom-up social generation of knowledge.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.209
GPT teacher head0.326
Teacher spread0.117 · 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 teacher head, 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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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