Learning in Crisis: Training Students to Monitor and Address Irresponsible Knowledge Construction by US Federal Agencies under Trump
Bibliographic record
Abstract
Immediately after President Trump's inauguration, U.S. federal science agencies began deleting information about climate change from their websites, triggering alarm among scientists, environmental activists, and journalists about the administration's attempt to suppress information about climate change and promulgate climate denialism. The Environmental Data & Governance Initiative (EDGI) was founded in late 2016 to build a multidisciplinary collaboration of scholars and volunteers who could monitor the Trump administration's dismantling of environmental regulations and science deemed harmful to its industrial and ideological interests. One of EDGI's main initiatives has been training activists and volunteers to monitor federal agency websites to identify how the climate-denialist ideology is affecting public debate and science policy. In this paper, we explain how EDGI's web-monitoring protocols are being incorporated into college curricula. Students are trained how to use the open-source online platforms that EDGI has created, but are also trained in how to analyze changes, determine whether they are significant, and contextualize them for a public audience. In this way, EDGI's work grows out of STS work on "critical making" and "making and doing." We propose that web-monitoring exemplifies an STS approach to responsive and responsible knowledge production that demands a more transparent and trustworthy relationship between the state and the public. EDGI's work shows how STS scholars can establish new modes of engagement with the state, and create spaces where the public can not only define and demand responsible knowledge practices, but also participate in the process of creating STS inspired forms of careful, collective and public knowledge construction.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".