The use of public opinion research by government: insights from American and Canadian research
Bibliographic record
Abstract
Political marketing research has previously discussed the use of focus groups and polling by political parties, but it has neglected to consider the substantial opinion research commissioned and conducted by government agencies. Government public opinion research (POR) is not well publicised, but provides a significant resource for politicians that can influence policy development, decisions and communication. Paraphrasing the Communications Policy of the Government of Canada (Treasury Board of Canada 2006), we define government POR as applied social science and marketing research using surveys and focus groups, commissioned by government agencies to map the attitudes and perceptions of citizens in order to produce policy-relevant information that will respond to the knowledge and marketing intelligence needs of policymakers and managers. This definition of government POR includes the gathering of information from civil society for evaluations; however, it excludes citizen consultations involving two-way communication between government and civil society through public hearings, web-based consultations or memoirs, even though some political actors view these state-citizen interactions as legitimate ways of knowing about public opinion on a given issue. Government POR is intended primarily for internal use to improve the knowledge base on which policy-makers and public managers conduct policy. Unlike political polling, which is not government-regulated, government POR is regulated at the federal levels in both Canada and the US to ensure political neutrality and methodological quality. Political neutrality requirements preclude government polling about voter preferences for political parties or candidates. Many of the uses of market research for a ‘permanent campaign’ presented by Sparrow and Turner (2001) would not be acceptable uses of government POR under current Canadian and US rules and regulations. This chapter will explore this hitherto neglected area of market research by considering government POR within a political marketing context.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.031 |
| Science and technology studies | 0.035 | 0.021 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".