Communication and language skills pay off, but not everybody needs them
Bibliographic record
Abstract
Abstract The importance of communication skills is increasing on the labour market and a further strengthening of this trend is expected due to Industry 4.0. This development will have significant consequences for individuals’ employability, requirements on educational outcomes and gender equality. This article employs data from a representative survey of Czech employees (N = 1,500) replenished with information on requirements on their communication skills (Effective communication, Czech language and English language) in order to explore (a) the distribution of communication skills requirements on the labour market, (b) personal and job characteristics related to work positions requiring highly developed communication skills, and (c) wage returns to these skills. The results show that one standard deviation increase in job requirements on communication skills is connected with 5.8% wage premium. However, not everybody needs well-developed communication skills. Only a quarter of employees needs highly developed effective communication, Czech and English languages, while there is also a quarter of employees that needs only a very basic level of communication skills. The results also revealed that females perform more communication-intensive occupations than males do. Cognitive skills and the need to excel represent other significant factors correlated with higher job requirements on communication skills.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".