'Netwerkvorming is professioneel aanmodderen'
Bibliographic record
Abstract
De verwachtingen van regionale netwerken als antwoord op urgente problemen in de zorg en het sociaal domein zijn hoog, zo valt op te merken in bestuurlijke akkoorden zoals het Integraal ZorgAkkoord (IZA) en het Gezond en Actief Level Akkoord (GALA). De veronderstelling hierbij is dat een min of meer gedeeld beeld over wat er ‘regionaal’ nodig is netwerkvorming bespoedigt. De praktijk kenmerkt zich echter door een zekere traagheid, zo blijkt onder andere uit tussentijdse rapportages. Hoe komt de beleidswens van netwerkvorming tot uiting in het werk van lokale organisaties en professionals? En hoe kunnen beleidsmakers hier weer op reageren?
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.300 | 0.104 |
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".