Chronotopic Expertise: Enacting Water Ontologies in a Wind Energy Debate in Ontario, Canada
Bibliographic record
Abstract
Rhetorical studies of water-related controversies highlight multiple interpretations of water at stake. Yet nearly every dispute over water involves not just contested meanings but contested ontologies. This essay examines water ontologies in a controversy over water wells in Ontario, Canada, which residents claim were affected by pile driving for wind turbine installation. Drawing on Annemarie Mol’s theory of multiple ontologies and the Bakhtinian term, chronotope, I show how different water ontologies emerge from spatiotemporal orientations and shift how expertise is enacted. Common water ontologies, water-as-resource and water-as-chemical-entity, enshrine white settlers as experts, despite their different stances on the issue in question. Municipal leaders, corporate representatives, and community members enacted water as an entity knowable to technoscience and exploitable by humans. An alternative ontology introduced by First Nations leaders, water-as-lifeblood, emphasizes water as a sacred, life-giving force. Speakers authorize themselves as experts by enacting water differently.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.051 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".