Nine building blocks for internal oversight
Bibliographic record
Abstract
Veel maatschappelijke opgaven kunnen niet worden opgelost vanuit één discipline, domein of beleidssector. Organisaties werken daarom steeds vaker samen in organisatienetwerken. Het ‘klassieke’ toezicht is echter niet ingericht voor het toezien op en in netwerken waarin intersectoraal wordt gewerkt en waarin organisatiegrenzen diffuser worden. Niettemin heeft toezicht impact op het effectief functioneren van een organisatienetwerk en daarmee op de publieke waarde die het netwerk daadwerkelijk kan creëren. Patrick Kenis en Cor van Montfort deden ontwerpgericht actie onderzoek naar toezicht in en op organisatienetwerken in het domein Sociaal wonen. Dit onderzoek vond plaats samen met BZK, Aedes, VTW en zeven organisatienetwerken waarbij minimaal één woningcorporatie was betrokken. Zij kwamen tot negen bouwstenen om passend toezicht op en in organisatienetwerken vorm te geven.
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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.018 |
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".