Opaque transparency: How material affordances shape intermediary work
Bibliographic record
Abstract
Abstract How do the material aspects of intermediary work affect regulators, targets, and beneficiaries? To shed light on this question, we studied an information intermediary in the form of a website and the organizations who founded it. Specifically, we analyzed FracFocus, a self‐regulatory initiative with strong industry ties, charged with disclosing data pertaining to the chemicals used in oil and gas wells completed using hydraulic fracturing technology (fracking) in the United States and Canada. We found that between 2010 and mid‐2017, the vast majority of legislation in states and provinces where fracking actively occurred was updated to mandate or encourage disclosure via FracFocus, meaning that it had a considerable effect on the trajectory of official regulation on fracking disclosure. We also found that FracFocus disclosed important data but did so in a manner that limited accessibility and reduced the comprehensibility of environmental and public health risks to beneficiaries. Our analysis suggests that the public's experience of such a device is one of opaque transparency, in which the line between official and non‐official regulation is blurred. We traced these outcomes to the material affordances created by FracFocus.
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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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".