The socio-spatial politics of royalties and their distribution: A case study of the Surat Basin, Queensland
Bibliographic record
Abstract
This paper is concerned with the socio-spatial and ethical politics of redistribution, specifically the allocation of natural resources rents from political and economic cores to the economic and geographical peripheries whence the resource originated. Based on a case study of the coal seam gas sector in Queensland's Surat Basin, this paper focuses on the operation of the Queensland State Government's regional development fund for mining and energy extraction-affected regions. Employing an environmental justice framework, we critically explore the operation of these funds in ostensibly helping constituent communities in becoming resilient to the worst effects of the ‘staples trap’. Drawing on secondary demographic and housing data for the region, as well as primary information collected from key respondents from mid-2018 to early 2019, we show that funds were distributed across all of the local government areas, and allocated to projects and places primarily on a perceived economic needs basis. However, concerns were raised with the probity of the funds’ administration. In terms of recognition justice, the participation of smaller and more remote towns and local Indigenous communities was hampered by their structural marginalisation. Procedurally, the funds were criticised for the lack of local consultation taken in the development and approval of projects. While spatially concentrated expenditure may be the most cost-effective use of public monies, we argue that grant application processes should be open, transparent and inclusive, and the outcomes cognisant of the developmental needs of smaller communities, together with the need to foster regional solidarity and coherence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".