Change the medium, change the message: creativity is key to battle misinformation
Bibliographic record
Abstract
Marshall McLuhan’s groundbreaking work regarding the role of context and medium in communication is very relevant today. By limiting the medium of science communication to dense, jargon-rich academic journals, we restrict the impact of discovery to the scientific community. We are also allowing the propagation of misinformation, as the nonexpert is forced to resource unreliable media to answer their scientific queries. To compete with pseudoscience, we need to improve science literacy and make science accessible through the same media on which pseudoscience thrives. As scientists and educators, we believe it is our responsibility to reconceptualize science literacy as a lifelong process and take greater accountability over the future of science communication. We hypothesize that increasing the accessibility of scientific literature to the public through adopting mainstream media forms and increasing access to informal science education (ISE) opportunities will decrease the proliferation of pseudoscience. To accomplish this, we propose eight recommendations housed under three action areas: 1) modify undergraduate science education by increasing opportunities for informal science communication, 2) increase accessibility to informal science education, and 3) bridge the gap between formal and informal science learning opportunities.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".