Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How can we know a watery space? This contribution to the ‘hydrosocial Anthropocene’ focuses on techniques and methodologies for the ethnographic and historical investigation of riverine societies. Here I examine three ‘moments’ to explore how we can open a river to ethnographic and historical investigation. The first is swimming, and how this practical activity provides an insight into the character of the space and body of the river, its flows and currents. The second is encounter: the river as a meeting point for human community and its nurturing. The final moment is river as a ‘being’; here questions of a river’s legal rights and ownership come to the forefront. This trinity of approaches helps to shift our terracentric notions towards a more liquid appreciation of human life. Underlying this shift is the work of scaling. The activities on and around rivers and seas produce different levels and depths of engagements: some intense and close up, others making use of its immeasurable surfaces for long-distance movement. Scaling then is a composite technique for knowing about human life and its embeddedness in the liquid environment.
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.
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 | 0.021 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.033 |
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 it