Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generally speaking, perception includes individuals’ subjectivity in terms of how they see or assess the characteristics of a phenomenon. Risk perception is vital to understanding what risks people consider to be acceptable, and what risk reduction programs have a better chance of being accepted. Risk perception is influenced by a variety of factors including the kind of information available and how that information is processed; the personality and emotional state of the perceiver; their personal experiences and prejudices; and socio-economic factors, to name but a few. Risk perception, risk tolerance, and high or low risk-taking behaviors are all interconnected. The nature and consequences of a potential threat, as well as its proximity, also contribute to how it is perceived by society. In this era of social media, the media is vital to ensuring that disaster news is covered more objectively. This chapter includes survey-based studies conducted in Canada as powerful testimonies to the importance of risk perception among various groups, including average citizens and emergency managers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it