Vulnerable persons in society: an insider’s perspective
Bibliographic record
Abstract
Purpose: Self-reliance and social participation are strongly promoted by social policy. Both concepts are linked to the concept of vulnerability, for people who do not meet these standards are labelled “vulnerable people”. In this paper, the insider’s perspective takes central stage by seeking to explore what it means to be labelled a “vulnerable person”, and through this to further our insight into the meaning of the concept of vulnerability.Method: Thirty-three in-depth interviews were conducted with 16 allegedly vulnerable people. The data were subjected to thematic content analysis.Results: Our analysis revealed three main dimensions and eight sub-dimensions of perceived vulnerability, outlining an insider’s concept of vulnerability. This concept includes manifestations of vulnerability, feelings coexisting with vulnerability, and the image of vulnerable people.Conclusion: The perception of vulnerability changes when interacting with others in society, especially with social policy implementers. In this interaction, the perceived vulnerability increases and becomes societal vulnerability. It concerns a dependency situation in which one’s strength and self-determination are not recognized, and the help needed is not provided. By acknowledging the insider’s perspective, social policy can fulfil a more empowering role towards “vulnerable people” and contribute to people’s well-being.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.060 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".