Grand Intentions, Small Interventions: Climate Data Rescue as Counter-Data Action
Bibliographic record
Abstract
Anticipating the incoming Trump administration’s hostility to climate science, the University of Toronto launched the first “data rescue” event in December 2016, creating a template for a kind of activism it labeled “guerilla archiving” to describe volunteers’ tactics of seeding, scraping, and bagging to disperse federal scientific climate data, documents, and webpages into an international patchwork of repositories. In recent months, similar events cropped up across the United States, guided by the Environmental Data Governance Initiative's (EDGI) Data Rescue efforts. The need for such work became palpable as official statements on anthropogenic climate change began disappearing from governmental websites, within hours of Trump’s inauguration ceremony. “Guerilla archiving” is a neologism – a critical term missing from archival literature. This paper examines Data Rescue’s guerrilla archiving efforts to situate the term within archival and critical data discourses and highlight its novelty as a contemporary case of both. Archiving in the face of political expediency is common in many types of radical archival projects. However, while radical archival work seeks to pluralize a community’s narrative through alternate stories and interpretations, EDGI’s web archiving and mirroring pluralizes and distributes the material context of the data. It distributes the data as a public good, generating occasions for data literacy projects which re-envision power and political action related to these datasets. In this sense, “guerilla archiving” exists as a unique example of counter-data action and statactivism, as it imagines new interventions to reconfigure power through distributed data management and use.
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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.069 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| 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".