Individual Giving: Theoretical Discussions and the Evidence from Serbia and Canada Ethical Issues, Contextual and Individual Factors of Giving Time and Money to Organisations and People
Bibliographic record
Abstract
This thesis focuses on individual giving, defined as voluntarily dedicating one’s non-material and/or material resources for the benefit of others or the common good. My research seeks to examine whether we are morally obliged to give, and it also strives to explain the factors that influence individuals to give. In searching for answers to the first question, I have discussed normative theories in ethics. All analysed normative theories maintain that we are morally obliged to help others, but they differ in respect to who these “others” are and for what reasons we should help. My research furthermore investigates contextual and individual factors that shape giving. Whether and how one engages in giving depends on her awareness of the need for help, then on her motivation as well as the personal and social resources she has a command of, as well as on an institutional environment, in terms of welfare systems, governmental support to the non-profit sector and the characteristics of the non-profit sector. Finally, my research provides evidence on volunteering for organisations, participating in the activities of informal groups, helping people directly and donating money to organisations and individuals in Serbia and Canada. This is the first such type of encompassing research on individual giving conducted in Serbia. Placed in a comparative perspective, it provides valuable insights. The rates of all types of individual giving that are analysed in both countries are higher in Canada than in Serbia, while differences in giving to organisations are particularly prominent. Most volunteers and donors in both countries give their time and money to similar causes, related to health, social services, education, religion and recreation. Both in Serbia and in Canada, most volunteers reported making contributions to the community as the reason they dedicate their time, while, reportedly, most donors give because they feel compassion towards people in need. In general, respondents who have a command over greater levels of personal resources are more likely to give both time and money than those with lower levels. However, not all resources are predictors of all forms of giving in each country. My research confirms that in order to gain an encompassing picture of individual giving in a country, as well as meaningful international comparisons, country-specific forms of giving must be considered.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".