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Record W2916934709 · doi:10.5334/kula.10

Artistic Research Creation for Publicly Engaged Scholarship

2019· article· en· W2916934709 on OpenAlexaffvenue
Jon Bath

Bibliographic record

VenueKULA knowledge creation dissemination and preservation studies · 2019
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsExhibitionMetaphorScholarshipRendering (computer graphics)Computer scienceThe artsWorld Wide WebDigital scholarshipMobile deviceSociologyMultimediaVisual artsPolitical scienceArtComputer graphics (images)

Abstract

fetched live from OpenAlex

In this paper I discuss the adoption of artistic research creation methodologies, the creation and exhibition of artistic works closely aligned with scholarly research, as a way to increase public engagement with academic research. I begin by discussing the need for scholars to consider the ‘public first’ when developing research communication plans, and draw upon the emergence of ‘mobile first’ interface design as a metaphor. With mobile first development, also known as progressive enhancement, ‘You start by establishing a basic level of user experience that all browsers will be able to provide when rendering your web site,’ but you also build in more advanced functionality that will automatically be available to devices, such as desktop computers (W3C 2015). I argue that we need to prioritize public first research outputs if we are truly serious about engaging the public in our research. I then move into a discussion of various research creation methodologies and explain how they are similar to, and differ from, critical making, another emergent humanities research practice that is based upon the making of physical objects. Finally I provide examples of successful research creation activities, including some related to my current SSHRC-funded project, The Post-Digital Book Arts.

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)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.211
GPT teacher head0.491
Teacher spread0.280 · 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 designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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