New Models of Collaboration - A Guide for Managers
Bibliographic record
Abstract
Governments around the world are attempting to improve public services through the use of advanced information technology. Increasingly these efforts rely on cross-boundary collaboration among government agencies, the private sector, and non-profit organizations. This guide focuses on the key elements of these new working arrangements of particular importance to the people who will design and manage them. It is based on the two-year multinational study New Models of Collaboration for Delivering Public Services conducted in a partnership among the Centre Francophone d'Informatisation des Organisations (CEFRIO), in Quebec, the Center for Technology in Government in the US, and the Cellule Interfacultaire de Technology Assessment (CITA) in Belgium. In the last decade, both industrialized and developing countries have been seeking new organizational models involving collaboration across-government or public-private partnerships. The defining characteristic of these endeavors is the voluntary combination of separate organizations into a coherent service delivery system supported by advanced information technologies. The rapid evolution of these technologies has created important new opportunities for governments to redesign services through creative relationships with other organizations. This guide is based on a multinational research project designed to understand how these collaborations work. It involved a network of field researchers in Canada, the US and Europe who studied more than a dozen collaborations and uncovered critical success factors and lessons learned about these new organizational forms are designed, managed, and perform. Twelve of the case studies are presented in this guide, along with discussions of four key management issues, and summaries of conference presentations and other research results.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.025 | 0.061 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".