Workforce churning, human capital disruption, and organisational performance in different technological contexts
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
Abstract We assess the influence of workforce churning on the relationship between organisational human capital and labour productivity. Building on collective turnover research and human capital theory, we examine how the components of workforce churning (i.e., voluntary turnover, involuntary turnover, and new hires) influence the relationship between existing human capital and labour productivity. Further, we examine how this influence varies according to a firm's technological intensity. Our data come from 1,911 Italian manufacturing firms and reveals that collective voluntary turnover negatively affects the relationship between organisational human capital and labour productivity regardless of an organisation's level of technological intensity. In contrast, collective involuntary turnover enhances the relationship between human capital and labour productivity, and its effect is even stronger for organisations with more technologically intensive operations. Finally, our results suggest that the integration of new hires disrupts the relationship between human capital and productivity, particularly for firms with technologically intensive operations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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