"We need more data!" The politics of scientific information for water governance in the context of hydraulic fracturing.
Bibliographic record
Abstract
Proposed and actual developments of hydraulic fracturing, as a high-volume water user, have proven contentious in recent years. However, one point of agreement has emerged amongst all actors with regards to water use and hydraulic fracturing: we need more data. This consensus fits with a longstanding reification of the role of data in water governance, and yet we argue it hides a politically contested terrain. Based on a literature review, an empirical Delphi study and a workshop with a diverse array of participants from across Canada, we explore the data needs related to water governance and hydraulic fracturing. We then investigate three areas of deficiency that point to a lack of trust and oversight as well as the exclusion of community and Indigenous knowledge. We argue that in an era of neoliberal approaches to water governance, issues of trust, accountability and transparency all link back to a diminished role for data management within existing water governance arrangements. The challenge is that simply collecting more data will not help decision-makers navigate the complexity of water governance. Our findings suggest a growing call by participants for greater engagement by governments in data collection and knowledge management, new funding mechanisms for data collection and rethinking how and what to monitor if including multiple ways of knowing and values.
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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 | 0.142 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.021 | 0.087 |
| Scholarly communication | 0.030 | 0.035 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.012 | 0.020 |
| 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".