Government with a Cast of Dozens: Policy Capacity Risks and Policy Work in the Northern Territory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are a number challenges to maintaining high‐quality policy capacity in sparsely populated areas such as Australia's Northern Territory (e.g. natural resource dependent economy, prominence of Indigenous issues, provision of local services). Moreover, the Territory government has recently been undergoing a host of public sector changes. This paper utilises survey methodologies of policy workers that were recently developed in Canada and examines nine risk factors to policy work. A survey of 119 policy workers in the Northern Territory was conducted in 2013. The analysis examined four key policy‐work areas (policy activities, barriers, areas for improved policy capacity, nature of change in work environment). The survey findings offer some practical insights for managers. Formal policy‐work training is recognised as critical. Policy capacity may be increased through better inter‐departmental (and potentially inter‐governmental) cooperation and information sharing, more opportunities to engage with non‐governmental stakeholders, and more opportunities for those leaving the full‐time Northern Territory policy workforce to continue to contribute. From a conceptual point of view, the extent to which ‘policy capacity’ as commonly conceived in the literature is applicable to contexts, such as Australia's Northern Territory, warrants further examination.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it