Exploring the potential of administrative data for understanding and advancing child protection and family support policy, practice and research in Ireland
Bibliographic record
Abstract
It is generally understood that administrative data at the level of the individual, family and wider population is fundamental to delivering client centred services which aim to support families and respond to, and reduce child abuse. They are valuable to policy makers and practitioners and play an important role in research. The focus of this paper is the potential use of administrative data from statutory family support and child protection and welfare services in Ireland for policy, practice and research. In the context of an evolving legislative and policy framework in Ireland, we provide an overview of the statutory family support and child protection services provided by Tusla Child and Family Agency. We suggest that this context provides an exceptional opportunity for developing administrative data sets in child protection and welfare and in family support. The benefits and challenges of developing administrative data sets are discussed. The paper concludes with recommendation for developing and linking administrative data sets to better understand and respond to the needs of children and families presenting to the services
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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 | 0.141 | 0.341 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".