Bibliographic record
Abstract
This paper outlines the experiences of a short artist in residency called Follow the water at the Vancouver Arts Centre in Albany, Western Australia that began in November-December 2018. Investigating the local network of urban and peri-urban drainage, the project was an attempt to reframe drains from what they are normally seen as—of a way of transferring ‘problems’ to elsewhere—into a space of reparative engagement. Intimate, makeshift walks were taken with drain allies along road culverts and agricultural drains and through snaky, polluted and weedy country. Walks were recorded with cyanotypes and a further cyanotype workshop was conducted with the public on the subject of local watercourses. Whilst being attentive to the local stories of water, settler history and regeneration, the project nevertheless attempted to problematise the current quasi-legal and commonplace notions which see the flow of water leaving a property downstream (and downslope) as being ‘not my problem’. In a small way, this art project works through the “impurity of caring” (that acts of caring contain the wish that it were not so (Shotwell), at the same time that they are entangled) with a tactical move that I have termed “porous repair.” It therefore provides a short example of the complications of thinking through water stories using artistic means.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".