Training as a Human Resource Strategy: The Response to Staff Shortages and Technological Change
Bibliographic record
Abstract
This paper examines the ways that innovation status as opposed to technology use affects the training activities of manufacturing plants. It examines training that is introduced as a response to specific skill shortages versus training that is implemented in response to the introduction of advanced equipment. Advanced technology users are more likely to have workers in highly skilled occupations, to face greater shortages for these workers, and they are more likely to train workers in response to these shortages than are plants that do not use advanced technologies. The introduction of new techniques is also accompanied by differences in the incidence of training, with advanced technology users being more likely to introduce training programs than non-users. Here, innovation status within the group of technology users also affects the training decision. In particular, innovating and non-innovating technology users diverge with regards to the extent and nature of training that is undertaken in response to the introduction of new advanced equipment. Innovators are more likely to provide training for this purpose and to prefer on-the-job training to other forms. Non-innovators are less likely to offer training under these circumstances and when they do, it is more likely to be done in a classroom, either off-site or at the firm. These findings emphasize that training occurs for more than one reason. Shortages related to insufficient supply provide one rational. But it is not here that innovative firms stand out. Rather they appear to respond differentially to the introduction of new equipment by extensively implementing training that is highly firm-specific. This suggests that innovation requires new skills that are not so much occupation specific (though that is no doubt present) but general cognitive skills that come from operating in an innovative environment that involves improving the problem-solving capabilities of many in the workforce. These problem-solving capabilities occur in a learning-by-doing setting with hands on experience.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".