The value of human capital within Canadian business schools
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically investigate whether an individual’s knowledge, skills and capabilities (human capital) are reflected in their compensation. Design/methodology/approach Data are drawn from university academics in the Province of Ontario, Canada, earning more than CAD$100,000 per annum. Data on academics human capital are drawn from Research Gate. The authors construct a regression analysis to examine the relationship between human capital and salary. Findings The analyses performed indicates a positive association between academic human capital and academic salaries. Research limitations/implications This study is limited in that it measures an academic’s human capital solely through their research outputs as opposed to also considering their teaching outputs. Continuing research needs to be conducted in different country contexts and using negative proxies of human capital. Practical implications This study will create awareness about the value of human capital and its contribution towards improving organisational structural capital. Social implications The study contributes to the literature on human capital in accounting and business by focussing on the economic relevance of individual level human capital. Originality/value The study contributes to the literature on human capital in accounting and business by focussing on the economic relevance of individual level human capital. It will help create awareness of the importance of valuing human capital at the individual level.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".