Navigating complex authorities: Intellectual freedom, information literacy and truth in pandemic STEM information
Bibliographic record
Abstract
Traversing scientific information has become increasingly fraught, as the new information landscape allows anyone to access endless information with a few keystrokes. However, those trying to find information, understand authorities and navigate experts need a deeper understanding not only of the information itself, but also of how and why information is shared. Increasingly, questions of expertise, locale and bias are driving the scientific information ecosystem and creating or expanding disinformation, misinformation and propaganda efforts. Librarians are in the centre of this maelstrom of information and are obligated to help people learn to be critical of information. This article presents an illustrative case study, using the example of scientific information around the safety and efficacy of the Oxford-AstraZeneca vaccine to demonstrate how modern scientific information sharing is shaped by the ways in which misinformation and fake news spread.
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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.059 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".