Különbségek és hasonlóságok a magyar és külföldi felsőoktatásban tanuló hallgatók önkéntes tevékenysége és motivációja között
Bibliographic record
Abstract
Voluntarism is a quite current topic.However, it is still not as widespread in Hungary, our target country, as it is in other countries, such as in the United States, Canada, or some Western-European countries.In recent years, information about voluntarism has started to spread and it has become a better known phenomenon.Younger generations, especially high-school students are highly involved in and are taught about voluntarism.Therefore, it is worth studying if they continue their voluntary work or not later on, when they enter higher education.The aim of the current study is to determine whether higher education students carry out any voluntary activities or not.If they do, an additional aim is to define the main motivation of these students for voluntarism.In the paper, voluntarism, voluntary work opportunities, voluntarism in higher education and motivations are discussed.In order to get current results, six focus group discussions were conducted among Hungarian higher education students, as well as foreign students studying at a Hungarian university.The results of the survey are presented and interpreted in the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.016 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.036 |
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; both teacher heads agree on what is shown here.
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".