Bibliographic record
Abstract
The Netherlands is a prosperous country. An extensive healthcare system aims to guarantee the conditions for a healthy, dignified existence. Despite extensive knowledge, it is not possible to create reasonable life prospects for everyone. This dissertation examines how we can improve the conditions for development and growth in society. The research focuses on homeless people in social shelters. It looks at the interaction between shelter users and their environment from different perspectives. Shelter users experience many problems in different areas of life. Despite good intentions, local service networks are unable to meet the essential needs of shelter users. This leads to all kinds of conflicts with their environment. The research shows the systematic nature of the system's failure. At the same time, it shows how joint learning processes in meaningful relationships can contribute to strengthening the possibilities and resilience in networks. This dissertation sheds new light on our social challenges. The approach developed offers concrete tools to give substance and direction to joint learning processes that strengthen the resilience of people and the networks around them. The data was collected among shelter users, but the pattern-oriented approach reveals universal processes in social interactions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.413 | 0.088 |
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".