Making Research: An Analysis of Arts-Based Practices in the Academic Process, A Case Study of Methods of Inscription
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.029 | 0.040 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".