The Mechanism of Identification and Management of Risks Affecting the Process of Supporting Creativity Based on the Sample from the Slovak Academic Environment
Bibliographic record
Abstract
This article focuses on risks while supporting creativity. This represents a knowledge gap that is addressed. The employees’ creativity is desired, but there is often no approach process to its support. The implementation is affected by risks needed to be managed. The aim was to create a mechanism for managing risks within the support of creativity in organizations, including commercial companies and others, e.g., sports clubs. Content analysis, case studies, questionnaire surveys, or models were applied. The results combined secondary (cases) and primary data (survey with two groups of respondents). The findings showed that when creativity is supported, people are willing to increase their performance (50% of academicians, 88.78% of students). The process is negatively affected by the lack of managerial skills and the interconnectedness of processes. Organizations should increase their managers’ skills. A proactive approach to risk prevention leads to continuous improvement. A procedure was selected when the potential of applying findings from the academic environment to other organizations was identified. A generalization of the findings was performed so that the research results can be applied in different environments after considering their specificities. The recommendations include the process for supporting creativity, the identification of risks, and the risk management mechanism.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".