Care Leaving and Social Capital: Reflections on Findings From an Exploratory Intercountry African Study
Bibliographic record
Abstract
ABSTRACT Theorizing continues to be a challenge within the burgeoning field of care leaving. This article considers whether ‘social capital’ contributes to explaining the care‐leaving experience. Various views of what constitutes social capital are explored, and a three‐category typology is presented. Social capital theory is explored through the application of descriptive themes from a study undertaken across four African countries. The study adopted a resilience outcome‐oriented mixed method design, which included peer researcher data collection, with 45 participants purposively selected from young people preparing to leave or having left the care of an international NGO providing a service in each country. Thematic analysis identified a series of cross‐national dimensions. Framing these descriptive themes within social capital theory shifts the focus of understanding young people's experience away from their personal capacity to cope with the transition to adulthood. Instead, it highlights the types, range and quality of relationships that enable or frustrate their transition. This refocusing not only prompts an explicit relational understanding of care leaving but also suggests ways in which research, service design and practice might usefully be developed.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".