Bibliographic record
Abstract
Two years ago, a nest box outside my window held a pair of Violet-Green Swallow. I counted six swallows fledge from the box and take their first flights in the July rain. Leaving the roof of the nest box, they flew in little loops out over the water, trying out their wings. I watched them from the dock, their bodies suspended in the air between the raindrops. This experience was the inspiration for what I call ‘nest-works’ – for poetic wilding of all-too-human spaces. Nest-works began with an experimental panel for the 2021 College Art Association Conference, called ‘Co-Making this World’. The experimental session was modelled after the nest of a bird, a Black-Capped Chickadee. As this cavity-nester builds a home of disparate materials, the panel of artist-researchers built a session of disparate theories and practices, as we considered relationships with world-systems that are in the process of making (such as the nest of the chickadee). For Technoetic Arts, we weave a new nest-work of research material, as we consider new models for knowledge and creative production. This nest-work is an entanglement of short essays made by artists working with a common pattern, framing eco-poetics on collaborative and participatory processes with the non-human/more-than-human.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.261 | 0.119 |
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".