Bibliographic record
Abstract
From 2024 and its spring débâcle to 2025’s first fall snow, an ethnographical collective of researchers from the University of Ottawa took off from campus for two ‘‘river semesters.’’ Following a speculative drop of water taken from the Kichissippi River (the Ottawa River in English or rivière des Outaouais in French), we fieldworked in, on and around water for an experience in elemental anthropology. We engaged with the various circulations sustained by the river flow, at times geo-chemical, at times eco-cosmological, always anthropogenic. From the sacred Anishinaabe island of Asinabka, to the adjacent massive dam of Chaudière Falls, through the headquarters of Brookfield Energy (a major hydroelectricity trading firm), to the multimillion-dollar riverfront development called Zibi—with its net zero dream of community living and neighbouring toilet paper factory that heats buildings in the winter—we regarded this sensitive anthropological confluence as a saturated flow (following Ruiz and Jue (2022)). A flow where water is, disparately and at times concomitantly, looked upon as a natural resource, a valuable landscape, a precious witness of perilous climate events to come, an alluring promise, a discomforting oracle or a forthcoming expansion of capital. Along the flooded banks of this continuously changing watercourse, which once was a highway for Indigenous peoples to travel, trade, and strive, and where the parliament of a rather young state now sits, we investigate the pulsating milieu where everything that is to come seems to run from.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.385 | 0.155 |
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".