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Record W2980379543 · doi:10.17613/bcsm-zj72

Historic Nova Scotia: Bridging the Gap with Digital Storytelling

2019· article· en· W2980379543 on OpenAlexaffabout
Roger Gillis, Sharon Murray

Bibliographic record

VenueHumanities Commons CORE (Modern Language Association / Columbia University) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNova scotiaCraftStorytellingDigital storytellingBridging (networking)Visual artsMedia studiesHistorySociologyLibrary scienceGeographyWorld Wide WebNarrativeArtEthnologyComputer science

Abstract

fetched live from OpenAlex

Historic Nova Scotia is a digital humanities and public history project that aims to bring community histories to life online (https://historicnovascotia.ca/). This paper will explore how collaborative, digital-storytelling can help bridge the gap between heritage theory and practice. We will provide an overview of the project followed by specific examples from the site/app that feature diverse ways of interpreting Nova Scotia's histories. As these examples will show, digital storytelling can shine a light on the everyday experiences of people in the province – including underrepresented groups – while tracing shared experiences, such as fishing, sport, and craft. By collaborating with, and showcasing the holdings of, museums, archives, libraries, and heritage organizations across the province, Historic Nova Scotia helps to increase their online presence, which, according to statistics, can bring people through their doors.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.369
Threshold uncertainty score0.743

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.004
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.043
GPT teacher head0.262
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 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
Published2019
Admission routes2
Has abstractyes

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