Regional development in a resource production system: long distance commuting, population growth, and wealth redistribution in the Western Australia Goldfields
Bibliographic record
Abstract
Abstract Resource towns often exist on a knife‐edge, largely depending upon global demand for their resource/s and, at the same time, playing a critical role in the development of a nation. The transition from single resource towns to diversified economies has been modelled on several occasions, but their application to other resource locales is difficult given the unique interplay of geographic, political, social, and economic factors. Nonetheless, Innis' Canadian staples theory may explain the political motivations of resource extraction and exportation, not least in relation to the Western Australia Goldfields. This paper seeks to explore the theory's potential in this context by examining the implications of high labour mobility. It employs a two‐step process using, first, a social network analysis to map the entire Australian labour commuting network and, second, a regression analysis of commuting, regional wealth, and population size against population change. While the Goldfields historically grew in line with processes described by Innis' theory, contemporary high labour mobility has created a variegated landscape of different development dynamics and trajectories. This finding carries implications for network patterns of residence and work. Labour acts to extend the distribution of wealth by sending incomes to the metropolitan core and to amenity‐rich regional towns across the State and nation. In such light, regional development scholars must view the resource town in its broader urban system of distinct but interlocked, and sometimes overlapping, activity nodes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".