Bibliographic record
Abstract
DRINK THE RIVER is a collection of moving image and sound-based works, developing ways of sensing, sounding, and relating to and with the River Eden in Cumbria. The collection seeks to respond to questions of “modern water” – exploring issues of representation and how abstraction leads to extraction from the river. The River Eden – 90 miles running south to north through the county of Cumbria – is one of the top 20 most polluted rivers in the UK. It is also my home. I was confronted with the ecological realities – dramatic flooding, sewage pollution, private land ownership – making it increasingly difficult to have daily encounters with this river. In DRINK THE RIVER (the collection of image and sound-based research projects that have grown out of this work) I became obsessed with the ways in which the River Eden is seen, understood, represented and framed, and how these ways of knowing nudge us towards certain ways of relating. For example, 90 MILES (2024) – an “armchair voyage” down the Eden – is the starting point and explores the abstraction of the river into maps and statistics – an abstraction that enables the separation from, and control over, the river. DRINK. DON’T SWALLOW! (2023) takes this abstraction even further, reframing the absurdity of our “modern” definitions, and the overspilling commodification of water. DRINK THE RIVER (2024) – the video game that the whole collection takes its name from – responds to separation, as barriers of ownership and pollution compelled us to rebuild the River Eden in this watery (re)imagined world. Contrasting the abstracted and extractive, in all works the body is centred. The methodology I developed throughout involved daily repeated simple acts of swimming in the Eden – this grew into the final piece in the collection, a line made by swimming (2024), not necessarily a film, but a documentation of a performance. Taking its name from Richard Long’s A Line Made by Walking, itself growing out of ‘simple creative acts of [swimming] and marking’… becoming ‘about place, locality, time, distance and measurement [and rhythm]’ and a messy (un)knowing of this leaky, diffuse, turbid, transforming river.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".