Be the Change You Want to See: How Commensuration Shapes Collective Action on Grand Challenges
Bibliographic record
Abstract
In this paper, we adopt a sociomateriality approach to investigate the relationship between commensuration and grand challenges. Specifically, we follow a multi-stakeholder partnership engaged in making the ‘health’ of a waterway system located in the Great Barrier Reef region commensurable, reifying it into a report card. Our findings show how ‘commensurating practices’ can define, regulate, and maintain what constitutes the grand challenge of water health degradation and what should be done in relation to it, while also “do nothing” as the data and the ratings depicted in the report cards are mostly ignored by those producing them and their stakeholders. Drawing on these insights, we reveal why and how divergent outcomes of commensuration arise in practice. Specifically, we uncover how actions of including and excluding data eventually coalesce into compromising accounts. These accounts can tie actors into ongoing efforts to alter the visual tool, even if the changes in question are ultimately ignored and sustain organizational lock-in. Our study offers key insights on the sociomaterial practices of making grand challenges commensurable which can indirectly help managers seeking to mitigate these problems.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.044 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".