Lessons from 5 years of GISERA economic research
Bibliographic record
Abstract
Scientifically robust analysis of trade-offs for onshore gas activity can inform the design of strategies for socially acceptable and efficient use of energy resources. Here, we present lessons from a portfolio of research spanning three States and different industry stages conducted as part of the Gas Industry Social and Environmental Research Alliance (GISERA). Considering the effects of onshore gas development on regional economies, an important lesson is to look at net changes, considering decreases as well as increases in economic activity. In Queensland, where competing claims about employment effects were raised in public debates, measuring reduced agricultural employment in addition to increases to the number of jobs in other sectors were crucial to providing a balanced analysis. Another lesson is to take a broad view of economic dimensions beyond employment and income. Our research shifted the public debate when we demonstrated that the construction phase in Queensland improved youth retention, gender balance and skill levels. Another lesson is that economic effects of gas development (positive or negative) can occur before stakeholders expect them. In New South Wales, we observed that the exploration phase had a significant positive effect on income (but not employment). A further lesson is that effects differ between domestic and export markets. Research from South Australia has demonstrated that the potential regional benefits of gas development substantially depend on meeting the energy needs of other local industries such as manufacturing. These lessons can inform public debate and policy settings and help balance different priorities such as energy needs, regional development and environmental sustainability.
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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 | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".