Subtle Matter and Delicate Bodies
Bibliographic record
Abstract
Stemming from an artistic mode of enquiry centred on the enmeshment of physical and virtual realms, four works are produced within seemingly paradoxical contexts of non-locality and site-specificity, distance and proximity, biology and data, and information as it pertains to technology and affect. The mediums of these works range from augmented reality, geo-locative technologies, sonic and spatial interactivity, dance choreography, 3D animation, public projection, and multimedia installation. Produced and exhibited in Kingston, Toronto, and Athens, I explore and define intersecting points of affect and materialism and the transmission of information. A new methodology is shaped through these stages of research creation, underscored by emergence, superimposition, offsets, and projection. Drawing upon concepts developed and articulated by Karen Barad, Teresa Brennan, Ursula Franklin, and David Bohm, specifically their understanding of interconnectivity on a material level, Subtle Matter & Delicate Bodies makes a case for a more empathetic, equitable ethos to be implemented in urban development and city planning, and social infrastructures, within the consideration of a more-than-human existence. Underscoring downstream consequences from a historical context, the works aim to tenderly touch upon lack, absence, reverence, and resilience. Through creation, an artistic methodology has formed that interweaves science and technology with poetic and responsive expression. This dissertation will also look upon the preceding research, studio experimentation, and exhibitions that have led to the four works of Subtle Matters & Delicate Bodies, and the approaches I have taken towards rendering invisible phenomena legible, felt, and considered.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".