Bibliographic record
Abstract
This paper aims to investigate the influence of skill selection on productivity gains.To do this, using the productivity of intermediate goods and the average level of technology models, we construct a model in which we show that the implementation of policy based on investment in large technological projects and the selection of the right workers for high-skill tasks left back by automation in technologically advanced firms are the key for the productivity growth.Our model indicates that the size of the firm's project affects the productivity gain.Less investment in technology adoption and creation by small firms generates less productivity.However, the investment in large projects through technology adoption from the leader or innovation via R&D investment enhances both firms' productivity growth and competitiveness and aggrandizes them technologically.The automation process in these firms leaves behind an immense pool of high-skill tasks that need to be filled with a qualified workforce.Thus, selecting the right workers becomes extremely important in productivity growth.The exit from the workplace of low-skill workers with obsolete knowledge will follow the need for high-skill workers with knowledge that suits the new technologies used in the firms making room for machines in repetitive tasks and high-skill workers in high-skill jobs.Besides, we find that high-skill workers increase productivity growth due to the high-skill jobs, which affects the firms' productivity growth.To put it simply, technologically advanced firms, to improve productivity growth, should adopt strategies based on selecting qualified workers that can increase the productivity of high-skill tasks.However, the education system should keep up with the new skill tasks generated by automation in training high-skill workers in the modern work market.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".