Public Trust and Regulatory Governance as represented through the Media
Bibliographic record
Abstract
Media coverage of politics often comments on decline of public's trust in institutions. There is a notion that public trust of is steadily decreasing. Many factors contribute to this reduced trust, including: unhappiness with performance, negativity of election campaigns, distrust of traditional political parties, scandalous behavior of officials (unethical, incompetent or corrupt conduct) and changing role of media. (1) The media is said to be more interpretive in its reporting and critical of politicians and government (2) and thus, politicians and are subject to criticism on a daily basis. The use of the eight-second spot, quotable quote, sound bite and live television in House of Commons (3) has assisted in turning politics into a public spectacle. The framing of political coverage in these negative tones stimulates public cynicism which leads to distrust in government. Past studies have suggested that increased public confidence in institutions, particularly regulatory agencies, results in increased public comfort with work of those agencies. (4) This is particularly important for regulators of new technologies such as agricultural or health biotechnology. In order for public to accept new technologies, a high level of public comfort is needed. Where regulatory processes are transparent and public is informed of new research and developments in regulatory process, public comfort (and public trust) increases. When there is a lack of trust in generally, and in regulatory systems specifically, producer and consumer utilization of new developments in biotechnology may decrease. There is no doubt that media exerts some influence on interactions between public and institutions. However it is unclear whether media shapes public opinion, or if media coverage is a mere reflection of public's opinion. This study examines level of public trust/confidence in regulatory agencies through public opinion data from 1990 to present. We collected and compiled data in three separate categories: politicians, civil/public service and regulatory agencies. We found that politicians have lowest levels of public trust, ranging from 18% to 46%. The civil/public Service has much higher levels of public trust, ranging from 47% to 72%. Finally, regulatory agencies (in this case Health Canada, Environment Canada and Canadian Food Inspection Agency) all maintained high levels of public trust, approximately 70% for every year surveyed. (5) The second part of this study examines newspaper coverage of Canadian regulatory agencies for agricultural and health biotechnology. …
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".