Citizenship information needs in the UK: results of a national survey of the general public by personal doorstep interview.
Bibliographic record
Abstract
This paper reports the results of the second stage of the Citizenship Information research project funded by the BLR&IC: a national survey, by personal doorstep interview, of the citizenship information needs of 898 members of the UK public. Major findings include: that the public obtain most of their information on current issues via the media, and that they generally feel well informed on these issues. The public believe, however, that government is not doing enough to inform them about the Single European Currency and local council cutbacks. Small proportions of the sample had encountered problems in relation to employment, education, housing or welfare benefits and had consulted a range of \ninformation sources in order to solve these problems. Over a quarter of respondents had \nexperienced disadvantage through a lack of access to information. Significant proportions of respondents were poorly informed about legal rights, welfare benefits and local politics. A highly significant majority (91.7%) believed that freedom of information was important for \nexercising their rights as citizens. Although access to computers in the home is presently limited, the majority of respondents indicated a willingness to use computers to vote and interact with government. Public libraries were the preferred source of government information and were seen as appropriate locations for a range of other types of citizenship information.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".