Основные направления инновационного развития регионального здравоохранения
Bibliographic record
Abstract
How do people cope when they feel uninformed or unable to understand important social issues, such as the environment, energy concerns, or the economy? Do they seek out information, or do they simply ignore the threatening issue at hand? One would intuitively expect that a lack of knowledge would motivate an increased, unbiased search for information, thereby facilitating participation and engagement in these issues-especially when they are consequential, pressing, and self-relevant. However, there appears to be a discrepancy between the importance/self-relevance of social issues and people's willingness to engage with and learn about them. Leveraging the literature on system justification theory (Jost & Banaji, 1994), the authors hypothesized that, rather than motivating an increased search for information, a lack of knowledge about a specific sociopolitical issue will (a) foster feelings of dependence on the government, which will (b) increase system justification and government trust, which will (c) increase desires to avoid learning about the relevant issue when information is negative or when information valence is unknown. In other words, the authors suggest that ignorance-as a function of the system justifying tendencies it may activate-may, ironically, breed more ignorance. In the contexts of energy, environmental, and economic issues, the authors present 5 studies that (a) provide evidence for this specific psychological chain (i.e., ignorance about an issue → dependence → government trust → avoidance of information about that issue); (b) shed light on the role of threat and motivation in driving the second and third links in this chain; and (c) illustrate the unfortunate consequences of this process for individual action in those contexts that may need it most.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".