Introduction: Interpretation in the Study of Australian Politics and Policy
Bibliographic record
Abstract
Amid the clamour of recent calls for evidence-based policymaking, citizen-centred governance, open government, and any number of other international trends in the business of public administration, another, less-heralded one has quietly taken root in Australia: a call to put interpretation at the centre of our analysis. As the self-made image of the objective civil servant slowly erodes in Australia, as elsewhere, there is growing acknowledgment that what the evidence says, how citizens should be involved, what open government means and entails, or indeed the significance and implications of any other trend in public administration, must be subject to interpretation of the actors involved. This is not to advance the notion of a new post-modern orthodoxy in thinking about Australian politics and policy—as fairly obviously no such orthodoxy exists—but rather to point out that an interest in subjective meaning is no longer the domain of the academic fringe. To mainstream policy and public administration scholars and practitioners alike, then, increasingly interpretation matters. That is not to say it didn't matter before – our contribution to this collection aims to show that to some extent it always has – but that the advent of ‘interpretivism’ (the inevitable ‘ism’ that emerged to attach itself to a particular interest of scholars in interpretation in politics and policymaking) has brought sharper focus to its significance, both in theory and 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".