Be careful what you wish for:Resource boomtowns and disillusionment in the Surat Basin
Bibliographic record
Abstract
Communities near resource extraction projects are notoriously prone to boomtown dynamics, and in the Australian context, the boom and bust cycles have shaped the nation's economic history. Australia is the seventh largest gas producer in the world, with production escalating after the establishment of onshore unconventional gas extraction in the early 2000 ' s. This paper examines the rapid development of onshore unconventional gas extraction, specifically coal seam gas, in the Surat Basin of Queensland, which is also the location of long-established and highly valued agricultural industries. The impact of neoliberal economic principles and public policies, broadly imposed from the early 1980s, caused many services to be rationalised across the region and smaller communities. A new industry, promising industry diversification, alternative employment and royalties, was promoted by the Queensland government and the gas companies in 2005. Very quickly however, tensions between the gas companies and the agricultural industry regarding secretive agreements, land use conflict and lack of consultation developed into blockades, protests and antagonism. Towns were also impacted with an escalation in land prices and competition for labour. Many residents of the Surat believed the government had been greedy in its haste to provide approvals with an eye to lucrative royalties, with little consideration for their welfare or their livelihoods. Resource economies throughout the world are replete with examples of this scenario. This paper documents the conflicts and the subsequent measures undertaken by government and the gas com-panies to appease the residents of the Surat, all of which took considerable time and expense. If more considered consultation and understanding had been developed prior to the first approvals being granted, this could have been avoided. This paper is timely, given that the Queensland has once again granted exploration licenses in the highly sensitive western Channel country without consultation or consideration, causing angst and uncertainty.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".