Divided loyalties, many hats, and punctuated worlds: the challenges of political, administrative and stakeholder collaboration for federal public servants in Canada
Bibliographic record
Abstract
While network studies have focused on mapping out the structural linkages between participants within a policy network, less attention has been paid to the behaviours of policy actors.Attention to network behaviour is important because it varies and with implications for the performance, legitimacy, and effectiveness of government.This dissertation seeks to examine actor behaviour by investigating the challenges, opportunities, and coping strategies of public servants who work in policy networks.Interviews were conducted with forty-five Canadian federal public servants across four horizontal initiatives: the Mackenzie Gas Pipeline Project, the Sector Council Program, Team Canada Inc, and the Federal Initiative to Address HIV/AIDS in Canada.Together with organizational documents and reports, these interviews highlight the limited ability of networks to support long-term policy development, translate political ambiguity into policy outputs, generate effective leadership, and adopt new collegial cultures.Reconfiguration of existing accountabilities, renewal of central agency support structures, and increased senior leadership might help public servants to overcome key network challenges: gaining inclusion, obtaining commitment, facilitating collegiality, and achieving agreement.This work highlights the importance of actor-centred understandings of collaboration.It reveals distinct challenges for public servants when they collaborate with other public servants, stakeholders, and political actors and uses a framework of rule contestation due to an institutional deficit to understand why they face these challenges.In turn, the concept of rule contestation raises important questions regarding the fit of current political and administrative arrangements for governance in an increasingly networked era.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.072 | 0.017 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 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".