"Bionic Bodies — Exploring 'Moist Media' at the Intersection of Psychedelic States, Technology and Queer Perspectives through Print Media Practises"
Bibliographic record
Abstract
This research explores the convergence of ‘wet’ and ‘dry’ modalities in visual and material processes to reimagine the ‘bionic’ body as a site of syncretic and transformative potential. Rooted in Roy Ascott’s theory of syncretism—finding unity between unlike things—and Legacy Russell’s glitch theory, the thesis creation positions non-binary and queer identities as active agents of disruption, remapping physical and virtual forms. These frameworks intersect with McKenzie Wark’s concept of 'hacking' as a mode of creating new relational planes, where seemingly disparate elements converge to unlock new possibilities for identity and representation. Through the integration of serigraph prints (‘wet’ processes) with digital editing techniques (‘dry’ spaces) and their iterative translation back into tangible outputs, the resulting giclée prints embody “fuzzy ambiguities” and “darting associations” that challenge fixed binaries. This process reflects a syncretic approach that mirrors the experiential overlap of psychedelics and digital interfaces, both of which mediate altered states of consciousness and modes of engagement. By visualizing and activating a ‘moist’ framework—an interstitial space between wet and dry—the research situates the body as a fluid site of innovation and resistance, negotiating tensions between materiality and immateriality. It invites critical engagement with systems of power, proposes reconciliations with ancestral and queer ways of knowing, and celebrates the inherent intelligences within glitch and disruption. This research bridges art, technology, and philosophy, offering a speculative vision of bodies as sites of continuous reformation and collective transformation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".