Public-Private Relations: Managing Inter-Organisational Relationships
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inter-organisational activity, whether public and private sector collaborations, university and industry partnerships or joint ventures between businesses, has benefits that range from increased market efficiency to innovative product development. Yet too often such activity can founder under the weight of differing expectations and divergent interests. How Organisations Connect shows how to avoid the pitfalls and make partnerships work.\nThe contributors, experts from a range of disciplines, demonstrate the importance of developing strategies and establishing infrastructures that enable organisations to connect, and communicate, effectively. Their insights are backed up by case studies that include an investigation of three government and community sector partnerships in Australia, Canada and New Zealand; analysis of what makes a university-industry collaboration successful; an exploration of the changing relations between central banks and governments in Australia and New Zealand throughout the twentieth century; and a study of recent innovative developments in the supply chain networks of some British consumer industries. Through economic and business theory, historical perspectives and contemporary evidence How Organisations Connect presents both fascinating research findings and practical advice
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.012 |
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 it