Careers in the environment in Australia: results of a survey of environmental jobs
Bibliographic record
Abstract
Internationally, commentators have identified a growing demand for environmental expertise. Matching this has been an expansion in the range of environmental careers available to workers: from environment protection and bio-physical areas, to local government operations, environmental auditing, assessment, and management. However, in Australia there is no overall picture of the types of jobs graduates in this sector have undertaken, which has limited the advice that can be given about environmental careers. To redress this situation a survey was conducted with 600 respondents working in the environment professions in Australia. The results identified a wide range of professional areas; these are grouped into 12 subcategories for three main environmental employment sectors: Environmental protection (19% of respondents), Conservation and preservation of natural resources (26%), Environmental Sustainability (55%). Respondents mainly had a bachelor-level degree; however, a substantial proportion had an honours degree or postgraduate qualification. Respondents strongly recommended undertaking work experience to acquire key general skills that they identified as important for working in the environment sector. A related suggestion was for tertiary environmental courses to have a practical focus that produces 'work-ready' students. Comparison with the situations in the UK, Canada and the USA and are also offered regarding the results and trends, and suggestions for further research.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".