Return on investment for workplace training: the <scp>C</scp>anadian experience
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 Canadian 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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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".