How do developed countries motivate volunteering: Comparative analysis of National Recognition Awards for volunteer in the United Kingdom, United States, Canada, and Ireland
Bibliographic record
Abstract
Purpose: With the rapid growth of volunteering in worldwide, the question of how to recognize volunteers at the national level to motivate volunteering has become a pressing matter. In a few of developed nations, volunteering is motivated by the establishment of national recognition awards for volunteer. As a result, the goal of this paper is to aid decision-makers in enhancing volunteering by drawing on the experience of these developed countries. Methods: This paper adopts a literature-based approach and presents a comparative analysis of national recognition awards for volunteer in the United Kingdom, the United States of America, Canada, and Ireland. The comparison factors include the objectives of awards, categorization criteria, eligibility prerequisites, nomination requirements, and the evaluation process. Following that, an examination of similarities and differences between the awards will be presented, and the article will end with some suggestions. Results: Each of these four countries has established standardized and effective criteria for volunteers’ recognition awards, despite that each country's practices vary to some extent. Based on these circumstances, nations conscious of the importance of volunteer recognition which should expedite the establishment of national recognition awards for volunteers, broaden participation, focus on the effectiveness of service, establish reasonable application standards while ensuring the transparency of the selection process, and actively seek to expand cooperation with other social organizations.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".