Social return and organisational culture
Bibliographic record
Abstract
Abstract ‘Social return’ (SR) is a term in the Netherlands that summarises all efforts to integrate people with a mental or physical handicap in the labour market. It is an important political topic because government wants not only an inclusive society but also a decrease of expenditures on social benefits; an important topic for employers, because organisations can profile themselves as socially responsible; and a topic for applied research, finding ways and means of realising the concept. The Rotterdam University of Applied Sciences is mainly involved because of the value of SR for applied research and the development of solutions that work. Several projects have been implemented with third parties, all of them involving students, e.g. through BA graduation research. However, the research also shows that there is no large-scale adoption among entrepreneurs yet. Three problems have been identified: (1) the SR policy currently has many negative side effects; (2) entrepreneurs must recognize that the involvement of employees with a SR indication not only costs money but may also contribute to profits; (3) insufficient attention is paid to finding the proper match between possible employees and suitable jobs (possibly with an adapted working environment). However, ‘social return’ is a feasible concept and the problems may be addressed. At the same time the initial efforts on realising ‘social return’ point at the importance of organisational culture. The main aim of this paper is to show the link between organisational culture and the successful implementation of social return.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".