The collective safeguarding responsibility model: The 12Cs: Development, evidence base and potential application
Bibliographic record
Abstract
Multi-agency (also referred to as inter-professional/inter-agency) collaboration is viewed as an imperative way of working to prevent and protect people from harm. The operationalization of multi-agency safeguarding, including the implementation of legislation and guidance, varies widely and there remain areas of ongoing learning in multi-agency safeguarding enactment. In addition to understanding the facilitators of collaborative safeguarding, we must also have tools to evaluate and scrutinize these arrangements, to maximize our effectiveness. This article follows on from a previous article (Ball et al., 2024a) and introduces the collective safeguarding responsibility model: the 12Cs. The 12Cs provides a unique, evidence-based, holistic framework that can demonstrate how safeguarding arrangements are working strategically and operationally, across and within organizations. The framework focuses on the role of practitioners and agencies in responding to safeguarding concerns, and crucially, the framework incorporates understanding the perspectives of those with lived experiences of receiving safeguarding support. The 12Cs can provide both a local and national understanding of what we have in place regarding multi-agency safeguarding. It also explores how this works, whether it is effective and what action is required to improve responses going forward. The multi-agency safeguarding landscape is a dynamic space, and as such, we must be able to continually assess and be assured of our safeguarding effectiveness to provide a robust evidence base to inform future practice.
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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 | 0.187 | 0.269 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".