Constantly changing, constantly adapting: Lives and life-course data of children and childhood social care
Bibliographic record
Abstract
ObjectivesChildhood social care can lead to lifelong problems affecting health, educational attainment and other areas of a person’s life. Improving outcomes for children are therefore key policy objectives in many nations. The aim of this workshop was to inspire and drive forward life-course research on childhood social care internationally. ApproachThe workshop included presentations on the themes of the data jigsaw across the family life-course and data sovereignty and engagement. Presentations under each theme were followed by group activities and discussions. ResultsAdministrative data collections that include longitudinal details on childhood social care, developmental, health and other outcomes are becoming increasingly available for researcher purposes. Examples include the BEBOLD platform and the New South Wales Child E-Cohort in Australia, ECHILD in England and the Welsh SAIL Databank. First nations data sovereignty and engagement of childhood social care recipients has become a norm in many nations and led to improvements in research, including in methodology and interpretation of results. Together, improved access to data and meaningful stakeholder engagement can inform policy and practice to improve outcomes for children and young people. ConclusionsBarriers exist to understanding the family and home environments, such as very limited or no information about the fathers and other household members. Capturing populations who move between jurisdictions, e.g. migrants, is limited as administrative data is difficult to share across devolved nations/federated states. In some cases, a mismatch exists between expectations of meaningful engagement and institutional/research culture, resource and skill availability.
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 | 0.033 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".