Size of the Company as the Main Determinant of Talent Management in Slovakia
Bibliographic record
Abstract
Nowadays, all sources in the reproduction process are easily substituted, thus the most important factors in reaching a competitive advantage are human resources. Talent management is the process oriented to enrich higher the ability of employers to increase their quality and productivity. Globalization has changed the structure of the companies in Slovakia, depending on the size of the company. This paper compares how the size of the company influences the main phases of the talent management process (strategy, identification, assessment, development, retaining). A scaled questionnaire was applied as a tool for data collection in 381 companies operating business in Slovakia. Questionnaire reliability was verified by Cronbach’s alpha. To verify the existence of statistically significant differences between individual groups of respondents, ANOVA was used. We found that the main differences between small and large companies were identified in the phases of talent identification and talent development. In bigger companies, management is more focused on HR plans that include talent identification and acquisition and have more possibilities to develop talented individuals. On the other side we could see that small companies were more successful in the process of retaining the talents. Talented people in small companies are more loyal to the employers and stay in the company for longer periods than talented individuals in large companies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".