MétaCan
Menu
Back to cohort
Record W4392568159 · doi:10.1386/scene_00063_1

Exploring art-making as research enquiry: Methods, materiality and intentionality

2023· article· en· W4392568159 on OpenAlexaffabout
David McFarlane

Bibliographic record

VenueScene · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIntentionalityMateriality (auditing)EpistemologyPsychologyAestheticsSociologyArtPhilosophy

Abstract

fetched live from OpenAlex

This article discusses the intersections of art-making and research enquiry, focusing on methods, materiality and intentionality. In response to the request to contribute to an archive of creative methodological processes, the author examines a methodology he is developing for co-creating artistic acoustic ecologies with the Great Lakes. It raises questions about the role of practice-based research in generating new forms of enquiry and systems of knowledge while examining the barriers to achieving legitimacy for artistic practices within the research landscape. The article explores ethical, technological and artistic considerations within the context of the author’s auto-ethnographic lens of an artist working in an era that demands verbalization as a primary articulation of knowledge and cultural expression. The author, a practising visual artist, musician and Ph.D. student in the research-creation-based Media and Design Innovation programme at Toronto Metropolitan University, discusses the need for concrete examples of how artistic research methods are effective.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.054
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0540.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.982
GPT teacher head0.807
Teacher spread0.175 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical · Methods

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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueSceneSame topicParticipatory Visual Research MethodsFrench-language works237,207