Are Australian jobs becoming more skill-instensive? Evidence from the HILDA dataset
Bibliographic record
Abstract
Labour market policy rhetoric since the 1980s has promoted the view that jobs inindustrialised counties, if they are to survive the pressures of global competition, will need toplace ever-increasing demands on the skills of the workforce. This paper describes a studydesigned to test this proposition on a representative sample of the Australian workingpopulation over the period from 2001 to 2005. The data come from HILDA (Household,Income and Labour Dynamics in Australia), a panel survey of some 6,000 households and18,000 individuals conducted annually since 2001. The dataset includes three indicatorsrepresenting a common metric across industries, occupations and levels in the workforcehierarchy of the degree to which jobs stretch the skill base of those who work in them,together with three variables covering task discretion and worker autonomy, which pastresearch has shown to be highly correlated with skill-intensity. These data make it possiblefor the first time to duplicate in Australia, albeit in lesser detail, the landmark research on theskills trajectory of the UK economy carried out over the last twenty years for the Economicand Social Research Council. Initial analyses suggest that in the aggregate, Australian jobswere less skill-intensive in 2005 than in 2001, a counter-intuitive trend for which anexplanation has still to be found.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".