Is it ‘you’ or ‘your workplace’? Predictors of job‐related training in the Anglo‐American world
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the determinants of job‐related training and workplace voice. Using data from a unique 2016 cross‐national survey of Australian, British, Canadian and American employees, the paper contrasts two classic formulations in the literature; (1) the neoclassical/human capital approach which predicts that individual characteristics (such as age and education) which increase the efficiency of learning, will have the largest impact on the allocation of training (i.e. younger and more educated employees will be afforded training) and (2) the traditional institutional approach which favors the structural characteristics present at the industry and firm level, the nature of the job itself and the strategic choices of firms as the major predictors of job‐related training. We find that age – a key factor in the human capital model – plays a significant role in the allocation of training but that education (in keeping with recent evidence) does not. In sum the human capital model provides, at best, only a partial explanation for the differences in training observed across individuals. In contrast, variables invoked by the institutional literature (i.e. occupation level; industry; ownership type; and market structure) are highly significant and account for a much greater proportion of the variance in training observed across workers. Other institutional factors such as the presence of a union and a human resource department were strong positive predictors of job‐related training. But most important were product‐market strategy and employee voice. Respondents working in firms utilizing a ‘high road/high quality’ product/service strategy and with a workplace consultative committee were significantly more likely to receive training than similar workers employed in observably similar firms. This last finding supports the industrial relations view of voice as an important channel by which training is optimally delivered inside the firm.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it