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Record W2655362743 · doi:10.1139/as-2017-0009

Worth a thousand words: visual collections and a long view of the North

2017· article· en· W2655362743 on OpenAlexvenueno aff
Leonard Kamerling

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsFilmmakingResource (disambiguation)Visual artsHistoryMedia studiesMovie theaterSociologyArtComputer science

Abstract

fetched live from OpenAlex

Historical film and media collections in the North contain an essential, indelible message for the future — that cultural knowledge is perishable and impermanent. Throughout the world as bearers of traditional culture pass away, much of their knowledge is lost. Film and audio collections can play a critical role in preserving living knowledge, allowing us to observe, experience, and study singular, irreproducible moments of a culture’s past. As time passes, these unique recorded moments take on a vital function; they become new conduits of knowledge, a visual and aural stand-in for real experience. This paper discusses the role of museum film and audio collections in preserving cultural knowledge and the challenges of extending this resource to the classrooms of remote communities throughout the North. The paper also discusses the collaborative cultural filmmaking initiative of Sarah Elder and Leonard Kamerling, their work with partner Alaska Native communities over a period of two decades, and their setbacks and successes in producing “authentic” records of Alaska Native life in the 1970s and 1980s, records that are now part of the Alaska Documentary Collections at the University of Alaska Museum of the North.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0010.001
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 head0.083
GPT teacher head0.279
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 teacher head, not a consensus.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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