Bibliographic record
Abstract
This hybrid paper/artist presentation is focused on the interactive documentary EXPOSED [https://unjustlyexposed.com], which provides a cumulative public record and evolving history of the coronavirus pandemic’s impact on incarcerated people. EXPOSED documents the spread of COVID-19 over time, inside prisons, jails, and detention centers across the US, from the perspective of prisoners and their families. Original interviews combined with quotes, audio clips and statistics, collected from a comprehensive array of online publications and broadcasts, are assembled into an interactive timeline that, on each day, offers abundant testimony to the risk and trauma prisoners experience under coronavirus quarantine. EXPOSED launched on October 30, 2020. It will be updated weekly until December 30, 2021. The scale of the project is intended to reflect the scale of the crisis. On September 1, 2021, there were well over ten thousand quotes, statistics, and audio clips in the project database. On July 8th alone, the timeline includes over 100 statements made by prisoners afflicted with the virus or enduring anxiety, distress, and neglect. The monochrome, image-less, headline-styled interface, which allows viewers to step through thousands of prisoners’ statements, is designed to visualize their collective suffering, signal that the injustices they endure are structural, and demonstrate that the criminal punishment system in the US, itself, constitutes a public health crisis.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.592 | 0.234 |
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".