Visualizing changes to US federal environmental agency websites, 2016–2020
Bibliographic record
Abstract
Websites have become the primary means by which the US federal government communicates about its operations and presents information for public consumption. However, the alteration or removal of critical information from these sites is often entirely legal and done without the public's awareness. Relative to paper records, websites enable governments to shape public understanding in quick, scalable, and permissible ways. During the Trump administration, website changes indicative of climate denial prompted civil society organizations to develop tools for tracking online government information sources. We in the Environmental Data & Governance Initiative (EDGI) illustrate how five data visualization techniques can be used to document and analyze changes to government websites. We examine a large sample of websites of US federal environmental agencies and show that between 2016 and 2020: 1) the use of the term "climate change" decreased by an estimated 38%; 2) access to as much as 20% of the Environmental Protection Agency's website was removed; 3) changes were made more to Cabinet agencies' websites and to highly visible pages. In formulating ways to visualize and assess the alteration of websites, our study lays important groundwork for both systematically tracking changes and holding officials more accountable for their web practices. Our techniques enable researchers and watchdog groups alike to operate at the scale necessary to understand the breadth of impact an administration can have on the online face of government.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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; both teacher heads agree on what is shown here.
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".