Bibliographic record
Abstract
The rise of global publics empowered ordinary people and made rulers nervous. It is hard to govern publics that are connected, opinionated, and know how to seek information. In states of emergency in particular—such as during wars, pandemics, and natural disasters, when the thirst for information is high and access to information is limited—things become more complicated. It was true in the late nineteenth century when modern media started to appear, and it is true in today’s age of digital communication. The instruments for influencing the public are not always conventional media that the state could or can use, seize, or control. In our digital age, anyone can spread information—and misinformation and disinformation—with few barriers. In a space where everyone is a publisher, news is improvised, fluid, and interactive and the line between producers and consumers of information is blurry. This is an ephemeral public. Content moderation, the practice of policing this unruly public, often fails. Neither ephemeral publics nor the struggle to police them is a novel phenomenon. In states of emergency, throughout modern history, the world went through similar phases of contentious public-making when trying to control information was like catching wind by hand.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.013 |
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".