Can Joined-Up Data Lead to Joined-Up Thinking? The Western Australian Developmental Pathways Project
Bibliographic record
Abstract
Modern societies are challenged by "wicked problems" - by definition, those that are difficult to define, multi-causal and hard to treat. Problems such as low birth weight, obesity, mental ill health, teenage pregnancy, educational difficulties and juvenile crime fit this category. Given the complex nature of these problems, they require the best data in order to measure them, guide policy frameworks and evaluate whether the steps taken to address them are actually making a difference. What such problems really require are joined-up approaches to enable effective solutions. In this paper, we describe a unique initiative to encourage a more preventive, whole-of-government approach to these problems - the Developmental Pathways Project, which has enabled the linkage of a large number of de-identified administrative databases in order to explore the pathways into and out of the negative outcomes affecting our children and youth. This project has not only enabled the linkage of agency data, but also of agency personnel, in order to improve and promote cross-agency research, policy and preventive solutions. Through the use of these linkages we are attempting to shift the paradigm to encourage agencies to appreciate that these "wicked problems" demand a preventive approach, as well as the provision of effective services for those already affected.
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 | 0.273 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".