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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".