Return on investment for workplace training: the <scp>C</scp>anadian experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the central problems in managing technological change and maintaining a competitive advantage in business is improving the skills of the workforce through investment in human capital and a variety of training practices. This paper explores the evidence on the impact of training investment on productivity in 14 C anadian industries from 1999 to 2005. Our productivity analysis demonstrates that in 12 out of 14 industries, training had a positive effect on productivity. However, when the analysis is put within a financial context, the return on investment was positive in only four industries. Faced with negative rates of return, why should managers in most of the industries in the study promote investment in training? Probably the best explanation is that new technology requires an investment in training. The investment in training is necessary just for the firm to maintain its current labour productivity. Employee turnover necessarily impedes the efficacy of training, because trained workers leave, and untrained workers arrive. Thus, training in this instance again is necessary just to maintain current labour productivity.
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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 | 0.001 |
| 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