Hybrid organisations and governance systems: the case of the European Space Agency
Bibliographic record
Abstract
The constitutive organisations of governance systems tend to multiply and diversify over time. In parallel, a tendency toward homophily favours the creation of clusters of homogeneous organisations. Yet, few systems drift to the point of disconnection or dislocation. Several remain sufficiently cohesive to allow adaptation and other complex properties to emerge. To maintain equilibrium between order and chaos, some organisations must create bridges between otherwise homogeneous groups. This paper argues that hybrid organisations are ideally suited for this role. By their nature, hybrids share characteristics with different types of organisations in global governance, allowing them to overcome strict homophily and create bridges across clusters. Hybrids benefit from acting as brokers and in doing so, they facilitate the exchange of material and ideational resources across the governance system. Even if it is not their intention, they contribute to holding governance systems together and counterbalance the effect of homophily. We illustrate this argument by examining the space governance system and the hybrid nature, bridging activities, and brokerage role of the European Space Agency.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".