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Record W6980393104

Careers in the environment in Australia: results of a survey of environmental jobs

2007· article· en· W6980393104 on OpenAlexaboutno aff

Bibliographic record

VenueRMIT Research Repository (RMIT University Library) · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Toxicity and Pharmacological Properties
Canadian institutionsnot available
Fundersnot available
KeywordsRedressSustainabilityWork (physics)Work environmentGovernment (linguistics)Natural resourceWorking environmentMatching (statistics)Environmental impact assessment
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.272
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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