Travelling meanings: accountability practices and standardised instruments at a child protection service in the Netherlands
Bibliographic record
Abstract
In social work across Europe, the formalisation of daily practices through online instruments, systems and protocols, when interlinked with performance targets, often causes friction with the complex reality of practice. A child protection service in the Netherlands aims to ease this friction by reworking their online instruments to prioritise the needs of families and professionals. Their revised instruments have multiple purposes: (a) helping families; (b) encouraging professional reflection; (c) creating outputs that allow for organisational evaluation practices. In this qualitative study, we seek to understand how professionals interact with these revised instruments, and how professionals anticipate the travelling meanings of the collected data as they are used beyond the family-professional interaction in accountability and evaluation structures. We found that professionals must account for various audiences, temporalities of paperwork and uphold their authority while working with the instruments. We argue that to ensure that the instruments become meaningful in practice, despite their travelling meanings, child protection service professionals engage in boundary work: navigating tensions between simultaneous calls for transparency, accountability, and efficiency on one hand, and reflecting and caring on the other.
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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 | 0.046 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 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".