Women and Men in Public Consultations of Road-Building Projects
Bibliographic record
Abstract
This paper discusses results of a research project designed to increase knowledge about women’s and men’s participation and their opportunities to take part in and influence the road planning process. The project was accomplished in an explorative case study, an advertisement study, and an implementation study that used questionnaires, observations, quantitative and qualitative analyses of conversations, content analysis of minutes, and advertisements. A basic principle of public participation argues that it should be inclusive and equitable to ensure that all interests and groups are respected. A literature study found that the subject of gender equality is basically nonexistent in the literature on environmental impact assessment. This project shows that about a quarter of participants at consultation meetings are women, but men talk longer and ask more questions. Those who attend meetings are generally older and have more education than the average person. Men and women bring up environmental and road safety issues during meetings, but men more often discuss economy, technical facts, alternative routings, and land ownership. Some participants had more experience taking part in public meetings and talking in front of other people. Participants with less experience seem to need more guidance and take a more active part in the meeting when a moderator leads the discussion. It is tempting to say that men are more experienced and women are less experienced, but that would be an oversimplification. The aim of increasing gender equality through an intervention study did not completely succeed.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".