<b>Making and Doing Politics Through Grassroots Scientific Research on the Energy and Petrochemical Industries</b>
Bibliographic record
Abstract
The high stakes of emergent environmental crises, from climate change to widespread toxic exposures, have motivated STS practitioners to innovate methodologically, including leveraging STS scholarship to actively remake environmental scientific practice and technologies. This thematic collection brings together current research that transforms how communities and academics identify, study, and collectively respond to contaminants engendered by the fossil fuel and petrochemical industries, including air contamination from hydraulic fracking, marine pollution from petroleum-derived plastics, and hydrocarbon derivatives such as formaldehyde that intoxicate our homes. These interventions make inroads into the “undone science” and “regimes of imperceptibility” of environmental health crises. Authors, most of whom are practitioners, investigate grassroots methods for collaboratively designing and developing low-cost monitoring tools, crowdsourcing data analysis, and imagining ways of redressing toxicity outside of the idioms of science. Collectively, these articles work towards remaking how knowledge is made about and across industrial systems by networking community grounded approaches for accounting for environmental health issues created by the fossil fuels and allied petrochemical industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".