But Have You Really Heard? Evaluating Respondent Contributions in Government Consultations and the Effects of Missing Details
Bibliographic record
Abstract
The Government of Canada has demonstrated that it is making an effort to be more open and consultative with its citizens through its membership with the Open Government Partnership.Although adaptations to evolving technologies have provided more opportunities for engagement, it is still questionable as to whether respondent voices are truly being heard.Through a case study on the consultations held for the drafting of the second National Action Plan on Open Government in Canada, this notion of respondent representation was explored.It appeared from the outset that there was overlap between respondent contributions and policy, but a more thorough analysis of the data demonstrated that the details of the respondent contributions were left out.As open government is still new, it can be concluded that positive and gradual progress has been made but there is still room for improvement should the Government of Canada intend to expand its participatory opportunities. But have you really heard? Evaluating respondent contributions in Government consultations and the effects of missing details 1 Chapter: Introduction"What We Heard" is a catch phrase that depicts an accumulation of contributions gathered through a series of public consultations.This catch phrase has been used more and more prominently in numerous contexts, but most importantly for this purpose, it refers to government and public interaction."What We Heard" is meant to show that governments are listening to respondents and reflecting their suggestions in change and ultimately through policy, thus creating a closer citizen-government connection.Through modern technological advancesspecifically the internet -more opportunities for participation have arisen, whereby citizens have the opportunity to participate both online and offline.This enables those who are unable to attend offline consultations to still have their voice expressed in an alternative fashion.The internet has revolutionized communication between citizens and governments, allowing multiple opportunities for interaction.Examples can be seen through sharing information on webpages, the use of email as an interactive tool, social media interactions and web page commentaries (Roy, 2006).These practices have made communication easier, faster and the act of retrieving information more accessible.Not only have governments adopted methods of online participation, such as the collection of respondents' input, but they also use the internet to broadcast calls for participation in offline consultations that expand their reach into the citizen population.Historically, traditional modes of consultation, like physical meetings, were used to gather input on various government activities from a group of citizens who were able to participate in person.However, the combination of both online and offline practices for government communication provides
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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 | 0.140 | 0.473 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".