Political economy, demography and development in Australia's Northern Territory
Bibliographic record
Abstract
The Canadian ‘staples thesis’ literature has documented both the risks (in the tradition of Harold Innis) and the opportunities (in the tradition of W. A. Macintosh) inherent in economies that are dependent on the export of minimally processed natural resources. The key risk is that of retarded long‐term growth as a result of a lack of diversification and over‐dependence on foreign capital and markets. This article argues that the demographic consequences of staples approaches to development also make it difficult to achieve diversification. It profiles Australia's Northern Territory as an example of a mining‐dependent (fiscal) economy that demonstrates a particular demographic profile consistent with what might be expected of a resource frontier. The article argues, however, that restrictive demographic characteristics persist (high sex ratios, high population mobility, disadvantaged position of indigenous people and remote dwellers) even though mining has become an insignificant direct employer (less than one percent of the workforce) and the services sector drives the labour market. This persistence can be linked to the Territory and federal government expectations of economic development patterns in the region and the frontier mythology created around the Northern Territory. Addressing the demographic imbalance is a critical step towards realizing ambitions for economic diversification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".