Bibliographic record
Abstract
This paper presents the project Hemo-resonance #1, the first in a series of art works that aim to open alternative pathways for thinking about and practicing diabetes. I begin by discussing the centrality of data collection via self-tracking for the management of Type 1 diabetes, and the ways this data collection orients understandings of diabetes and the diabetic body. Diabetic self-management is typically aimed at finding patterns in one’s data, establishing cause and effect relationships, and understanding trends in the body’s operation so that the diabetic can modulate behaviour to optimize health outcomes. Arguing that approaching data in different ways can provide insights into diabetic experience and relationships that extend beyond the goal-oriented approach of always doing better, I offer “data resonance” as a way of following other trajectories of data and bodies. Data resonance provides sensory-rich materialisations of data in ways that seek to detach themselves from the typical focus on the legibility or interpretability of the data. This suspension of habitual orientations to data makes space for thinking of bodies, data, and the relationships between the two in new ways, and offering meditations on the value of co-corporeality, human-non-human relationships, and bodily difference.
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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.021 | 0.079 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".