B’ the Change you Want to See: How Commensurating Water Health Impedes Collective Action
Bibliographic record
Abstract
We study commensurating in a multi-stakeholder partnership established to address waterway health degradation in Australia’s critical Great Barrier Reef region. Actors of this partnership engaged in commensurating this issue, reifying it into a report card. This tool became central in representing the group’s assessment of the sustainability issue and driving collective action towards mitigation. As actors sought to commensurate the issue, they increasingly became dubious as to the real impact of their tool. Even so, they continued to engage in counterproductive commensurating practices that limited their impact. We offer two contributions based on these findings. First, we extend our understanding of the pitfalls attached to commensurating sustainability issues by revealing how efforts put in altering a commensuration tool can gradually shift actors’ attention away from ensuring the tool enables them to take impactful collective action. Second, we open the commensuration “black box” by revealing the interactional dynamics nested within the commensurating of sustainability issues. Specifically, practices aimed at including additional sustainability data in the tool are often met with practices seeking the opposite, that is, to exclude data. These countervailing practices eventually coalesce into changes to the commensuration tool which generate ‘small wins’ for actors, diverting their attention away from ensuring the tool enabled them to take impactful collective action.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".