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Record W2564438063 · doi:10.22230/src.2016v7n2/3a248

History Moves: Mobilizing Public Histories in Post-Digital Space

2016· article· en· W2564438063 on OpenAlexvenueno aff
Matthew Wizinsky, Jennifer Brier

Bibliographic record

VenueScholarly and Research Communication · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsPublic historyCitizen journalismConversationSociologyPublic spaceDigital mediaSpace (punctuation)Media studiesComputer scienceWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Similar to most cultural forms today, history is collected, edited, manipulated, stored, displayed, distributed, and otherwise produced through a complex network of post-digital techniques and media. It is often produced only by historians. History Moves is a public history project that aims to produce cogent and collective historical experiences within the cultural frame of mutable and highly distributed media forms. It does this by bringing history, design, and historical subjects into conversation to shape public space. The project uses a participatory process that mobilizes people to interpret history by mobilizing digital and analogue media. History Moves transforms historical subjects into history-makers while simultaneously and repeatedly transfiguring media into forms that are engaging and accessible to widely distributed audiences. This article recounts a case study where History Moves worked with a group of women living with HIV/AIDS to present a history of the epidemic and a women’s history of Chicago. We suggest that the example provides a model for how to build participatory digital history projects and collaborative history displays.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.974
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.005
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.302
Teacher spread0.138 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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