Beyond the boundaries of science: Resistance to misinformation by scientist citizens
Bibliographic record
Abstract
In November 2016, small groups of coders, climate scientists, scholars, journalists, and activists came together under the umbrella organization Environmental Data & Governance Initiative (EDGI) to defend against the Trump administration’s attacks on environmental protections. A distinguishing feature of EDGI, and the focus of this study, is the methods it has developed to combat propaganda and misinformation around science. This article analyzes the push-and-pull dynamic between the values and practices of EDGI and journalism, as they interacted in the expanded political information news field. Drawing on EDGI publications and interviews with its members, this article details EDGI’s efforts to connect with journalists and the public. It explores EDGI news coverage, focusing on the manner and frequency in which EDGI values and perspectives were, and were not, conveyed in the coverage. We trace the development of what we argue are new forms of public intervention and offer an expanded understanding of the misinformation ecosystem.
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 | 0.034 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".