Skills Shortage in the Electrical and Associated Industries and Employers' Perceptions of Apprentice Training as a Contributing Factor
Bibliographic record
Abstract
A strong skills base and effective skill development are important ingredients for a country to successfully compete within a global setting (ANTA 2003, objective 4). The size of a skills base will be determined by the number of skilled workers presently in the workforce, their propensity to remain there, the number of new skilled entrants to the workforce and the rate of skill formation among workers. The incidence of newly skilled people will derive from the training effort in the previous period and/or increments to the population of skilled workers through migration. When the number of new entrants is not sufficient to offset the level of exits of a given skill, given the labour market needs for that skill, a skill shortage will develop. There is evidence of this in a number of trades within Australia at the present time, including the electrical trades (Financial Review 2002) as there is also for a number of other countries such as Canada, the United States and the UK where shortages of electrical trades-persons have recently been reported (Jenkinson 1997; Canadian Labour Congress 2002; Bond 2002; Wark 2002; P Sherwood 2001; Hillage et al 2002; Grant 2003, Anonymous 2002).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".