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Record W2313271742 · doi:10.14288/1.0102530

Institutionalizing Innovation in Municipal Governance A Preliminary Analysis of the Plateau-Mont-Royalʼs Comité aviseur Model

2016· article· en· W2313271742 on OpenAlexaboutno aff
Julien Landry

Bibliographic record

VenuecIRcle (University of British Columbia) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governancePlateau (mathematics)Public administrationPolitical scienceBusinessAccountingManagementEconomicsFinance

Abstract

fetched live from OpenAlex

Following its electoral victory in the Plateau-Mont-Royal in November 2009, the Projet Montréal political party has been experimenting with a new model of participatory governance based on the work of expert advisory committees, or Comité aviseurs. These committees are made up of volunteer residents, experts and professionals, and are mandated to develop project and policy proposals based on main issues and actions contained in the party’s electoral platform. Even though the experience is relatively short (approximately 8 months), implementing this model of municipal governance and decision-making has already brought both benefits and challenges. Indications of improved efficiency and collaboration with the public service have been met with some challenges in process and structure. This raises the questions of how to institutionalize participatory governance in the present context, how to do so sustainably, and how to monitor the process to ensure that it will lead to desired outcomes. This report seeks to answer these questions and provides a set of recommendations from which Projet Montréal may draw as they move forward with the implementation of Comités aviseurs. The report is based on a collaborative evaluation research methodology, which was developed and revisited in partnership with Projet Montréal officials. The research relies on data gathered in May and June of 2010 through focus groups, interviews, participant observation and a survey of committee members. A majority of members (59.3%) responded to the survey and provided the following responses: a) On the Comités’ Roles and Functioning: the levels of satisfaction and clarity about members’ roles and about the Comités’ functioning differ from one Comité to the next. Although there is no consensus, the most important role of Comités aviseurs is that of developing proposals. There is a high level (73.9%) of satisfaction with the process so far. b) On the Nature of Participation: motivated by an opportunity to effect change in their borough, most respondents feel they actively participate in, or have an influence on decision-making. Members are learning a significant amount of information, knowledge and skills about municipal governance. It remains unclear what the role of the general public is or ought to be in this process. c) On Strengths, Weaknesses, Best Practices and Main Challenges: strengths included efficiency, innovation, creativity, and collaboration between a diversity of residents and experts; weaknesses related to the balance between expert and non-expert citizens, unclear roles and information flow and lack of follow-up; best practices included involving public servants, sharing knowledge, fostering diversity and meeting regularly; and the challenges related to the bureaucratic environment, to monitoring the process, to ensuring clear communication and leadership. d) On Recommendations: Key recommendations included reaching out to a broader audience, establishing clearer criteria in member recruitment, as well as improving communication and follow-up. Complementary to the survey results, other findings were synthesized and presented as needs to be addressed in fostering the long-term viability of Comités aviseurs: a) Need for more clarity and follow-up in the process; b) Need for more formal meeting structure and process; c) Need for documenting and managing information; d) Need for more clearly-defined roles and responsibilities; e) Need for increased communication and coordination between Comités aviseurs; f) Need to incorporate public opinion (this may include framing the Comités aviseurs as public representation, increasing public access to the process, combining the Comités with public broader public consultation or inviting the public to participate directly in the process); g) Need for budget and resources; and h) Need to acknowledge and foster members’ motivation. These needs are currently being met and addressed to varying degrees. They represent Projet Montréal’s main considerations as they explore the sustainability of Comités aviseurs. These findings point to some of the common advantages and challenges of participatory governance. In support of institutionalizing this model of governance, the findings are analyzed within a good governance framework that uses efficiency, transparency and participation as its key indicators. The Comité aviseur model is found thus far to be quite efficient. Although the timeline is still rather short to properly assess efficiency, the impressive number and high quality of projects submitted to the public service are good indicators. With respect to transparency, the model performs very well with the citizens directly involved – that is, Comité members. There is some room for improvement with respect to transparency and accountability to the general public. The case is similar with regards to participation. Members are highly engaged in a participatory governance model while the public at large does not have much opportunity to contribute. There are many factors to consider in institutionalizing the Comité aviseur model. According to indicators developed by Irwin and Stanbury (2004), the Plateau-Mont- Royal is a fairly favorable context for participatory models of governance. This is also visible in analyzing the Comité aviseur’s strengths, weaknesses, opportunities and threats (SWOT): the strengths and opportunities are obvious and many of the weaknesses and threats can be addressed with more participation and better monitoring. Motsi’s evaluation framework for citizen engagement is presented as a basic starting point for monitoring the Comités aviseurs. It focuses on five components of a participatory exercise: a) Purpose b) Process c) People d) Context e) Outcomes The report concludes by acknowledging Projet Montréal’s willingness to be critical about its work. On the one hand, initial success, efficient work and the submission of high-quality proposals are encouraging. On the other hand, there remain the challenges of communication and coordination, clarity of roles and responsibilities, information management and the public’s role. According to a good governance framework, the model is faring very well for people who are directly involved. The recommendations provided below seek in part to extend access and benefits to the general population. Projet Montréal is doing commendable work in attempting to govern innovatively. In doing so, it will be important to be responsive to the evolving context and reality of the Comités aviseurs so that the model may be institutionalized, monitored and sustained for the duration of the electoral term. The following recommendations (with rationale and potential actions) are proposed to Projet Montréal as it embarks on its second year of working with Comités aviseurs: 1. Ensure clarity of process and regular follow-up with Comité aviseur members. 2. Formalize Comité aviseur meetings. 3. Clarify roles and responsibilities. 4. Centralize and manage information for transparency and accountability. 5. Ensure coordination and communication between Comités aviseurs. 6. Allocate appropriate resources to improve the governance model. 7. Integrate the Comités aviseurs within a broader public participation framework. 8. Establish norms for future Comités aviseur formation. 9. Monitor and evaluate Comités aviseurs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0070.013
Scholarly communication0.0120.005
Open science0.0020.008
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.021
GPT teacher head0.229
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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