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Record W3080478814 · doi:10.38055/fs010103

Making Research: An Analysis of Arts-Based Practices in the Academic Process, A Case Study of Methods of Inscription

2018· article· en· W3080478814 on OpenAlexaffvenueabout
Ketzia Sherman

Bibliographic record

VenueFashion Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTattoo and Body Piercing Complications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAdornmentExhibitionVisual artsContext (archaeology)The artsProcess (computing)Subject (documents)ArtSociologyComputer scienceAestheticsHistoryWorld Wide Web

Abstract

fetched live from OpenAlex

Researching within the field of fashion and the body means working very closely with the artistic community including fashion designers, illustrators, and visual artists. Despite this, research on the subject rarely utilizes arts within the research project. This paper aims to analyze a successful application of arts-based research practices within scholarly research. The project in question, Methods of Inscription, utilizes an arts-based research approach to explore the tattoo experience within a Canadian context. The body of work, developed for exhibition, combines primary and secondary research with artistic exploration to visualize the collective experience of tattooed individuals. The ways in which we understand tattooing and body adornment are directly linked within the study of fashion. Both visual art forms change the appearance of the body, consequently effecting one’s interaction with the world around them. The study of both fashion and tattoos can only be achieved through the use of an interdisciplinary research method, which acknowledges both visual outcome and lived experience. This paper will outline the significant writings used to support and analyze arts-based research practices, the methodology used in the creation of Methods of Inscription, as well as an analysis of the created artefacts, and the knowledge that they embody.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.827
GPT teacher head0.711
Teacher spread0.116 · 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 designQualitative
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
Published2018
Admission routes3
Has abstractyes

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