Embroidered portraiture and the intentional creation of human visibility and value
Bibliographic record
Abstract
Abstract The aim of my research was to create a portrait following a sequential practice-based process. A mug-shot photograph of a female face was analysed and reinterpreted as an embroidered portrait. Hand-stitched embroidery and embellishment techniques define the facial features and surroundings of the subject matter. The final representation encapsulates my total experience while researching this woman’s image in both a practical and a theoretical way. This encompasses the physical procedures of sketching, painting and embroidering her likeness, with a preoccupation and self-awareness necessary to understand the theories engaged with during these processes. I used an arts-based framework, looking at Michael Podro’s writing ‘Depiction and the concept of subjectification’ in interpreting the portrait subject. In my analysis of the mug shot I drew on Roland Barthes’ theories of ‘essence’ or ‘air’ of a person and his concept of ‘punctum’, the personal and unexplained emotional response caused by a photographic image. Similarly, Gilles Deleuze’s concepts of ‘affection-image’, the ability for a photograph, specifically the extreme close-up of the face or ‘traits of faceiticity’, to move, touch, disturb, disgust... to profoundly affect the viewer with the ‘micro-expressions’ and fine detail magnified in her frozen look. I also referred to Claire Valier’s writings on the ‘punitive power’ of the documentary photograph to punish, shame, shock, elicit guilt or incarcerate as a means of social commentary and an agent of justice (Valier 2004: 254). These philosophies informed the potency and persuasiveness of the photographic content. The picture acts as a visual record of an actual moment that facilitates belief in the accuracy of the event depicted. With this, my female subject represented in the mug shot was a criminal, a prostitute and there was incriminating physical evidence of drug addiction. By transforming the mug shot to a handcrafted embroidered portrait, I intended to remove the stigmatized version of the women. While doing so, I willingly addressed the issues of agency in the subject, viewer and image–maker relationship. These concepts brought forth an intense analysis of my motives and intentions in making the portrait, which eventually lead to an understanding and development of a process that revealed my own translation of my subject’s humanity.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".