Future Demand for Skills in New Zealand Compared with Forecasts for some Western Countries: Relative Importance of Expansion and Retirement Demand
Bibliographic record
Abstract
Future demand for skills is of considerable interest to policy makers and training providers of many countries, including New Zealand. Occupational employment projection have been implemented in countries such as the US, UK, Canada and Australia. These methods usually take into account growth in GDP of key industries, changes in labour productivity and the longterm changes in the occupational shares of employment by industry. In New Zealand, an assessment of the future prospects for employment by industries and occupations comparable to the overseas approaches has been undertaken over the past few years. These estimates have been used to assess the skills needs in the expanding segments of the labour market. In this paper we compare our forecasts of occupational employment growth with public sector agencies in other countries. The key forecast results of demand for high level skills, for specific broad occupational groups as well as for industry or sector groups for each of these countries as they recover from the economic downturn of varying magnitude and nature are discussed. We focus on both the expansion demand (due to new positions created) and the replacement demand (owing to current positions being required to be filled due to retirement, migration and job mobility) for New Zealand and other countries as applicable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".