PIVOT New Models of Collaboration for Public Service Delivery Worldwide Trends
Bibliographic record
Abstract
still in the early stages, but the research team has already conducted a preliminary review focusing on the status of knowledge regarding alternative public service delivery methods. This working document summarises the findings of this first step. It should be viewed as a starting point rather than a conclusion. This document is therefore designed to be a draft which will be detailed and completed over the coming months. This document is a first draft that will be edited and completed during the coming months. This report contains four sections. The first section is divided into two parts: a brief history that provides a better understanding of the contextual factors that have influenced government policies in terms of public service delivery; and a inventory of the trends in terms of solutions adopted by governments. The second section of the report defines collaboration within the context of public service delivery and delineates the boundaries of this interorganizational collaboration. The third section introduces a conceptual model for the study of new models of collaboration and briefly describes the success factors identified in the literature. The report concludes with an overview of the situation in Australia, the United Kingdom, the United States, Canada and a few developing countries and newly industrialised economies. The main research team was composed of Professors Line Ricard, Hélène Sicotte and Lise Préfontaine as well as Research Professional Danielle Turcotte. Other members of the PIVOT Research Team participated in the research endeavour: Professors Mario Bourgault (École Polytechnique), Yves-Chantal Gagnon (ÉNAP) and Elizabeth Posada (UQAM), joined by Professors Andrée De Serres (UQAM), Luc Bernier (ÉNAP) and several students from the aforementioned universities. The segment on the United States was written by
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".