Human resource development in SMEs in a context of labor shortage: a profile analysis
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
Purpose The purpose of this paper is to examine small and medium-sized enterprises’ (SMEs) level of participation in human resource development activities during a labor shortage. Drawing on human capital theory, it examines whether SMEs’ profiles, determined according to their participation in different types of training activities, relate to perceived benefits of training, barriers to participation in training and learning culture. Design/methodology/approach This study applies latent profile analysis (LPA) to 10 training practices of 427 SMEs in Quebec, Canada. Findings The LPA distinguished four profiles of SMEs, reflecting differing capacities for mobilizing training resources during a labor shortage. These four profiles show differences with regard to perceived training benefits, barriers to participation in training and learning culture. Originality/value To the best of the authors’ knowledge, this study is among the first to focus on the specific ability of SMEs to invest in their human capital in the unique and recent context of a labor shortage.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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