Analysis of a young Polish firm's use of Green HRM practices with the purpose of achieving sustainable business development
Bibliographic record
Abstract
The concept of incorporating environmental practices into human resource management, commonly referred to as Green HRM, has become increasingly popular. This is because sustainable development, particularly its ecological aspect, plays a crucial role in establishing a competitive edge for modern firms. The objective of the study was to compile a comprehensive inventory of the environmentally conscious human resource policies and processes used by startup enterprises in Poland, and subsequently assess their impact on the long-term sustainability of these organizations. In order to accomplish these objectives, a survey was conducted on a representative sample of 150 young enterprises. Based on the findings, the concept of Green HRM is relatively recent in Poland. There exists a strong correlation between the evaluation of the impact of particular Green HRM initiatives on the long-term sustainability of businesses and their subsequent implementation. The research indicates that a higher rating of an activity's impact resulted in its more frequent implementation in the evaluated firms. Based on the aforementioned analysis, it can be inferred that the dissemination of information regarding the favorable impacts of Green HRM on the sustainability of organizations is crucial in fostering greater adoption of this concept among young Polish enterprises.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".