Change in Volunteering Roles Managing Change to Build Volunteer Capacity
Bibliographic record
Abstract
The roles of volunteers, like those of employees, may be subject to change. While the importance of assisting paid workers to manage change is well recognised, less attention has been paid to understanding and responding to volunteers' experiences of change. Change, if effectively managed, can be a positive force for building volunteer capacity and sustainability in volunteering organisations. Conversely, poorly managed change can limit the capacity of the organisation to effectively build and involve volunteers. In essence, change in volunteer roles may enhance the volunteer experience or lead to increased tensions within it. In this paper, we will draw on a three-year study on enhancing volunteer capacity in three diverse volunteer-involving organisations. We report on findings from responses from 454 volunteers about their perceptions and expectations of change. We analyse how perceptions and experiences of change vary according to organisational type, type of volunteer activity, and demographic factors, specifically gender, age and country of birth. We found statistically significant relationships among these variables suggesting considerable differential impact of change on various groups within the volunteering population. The study finds that half the volunteers felt their roles and responsibilities had changed since they began volunteering with their organisation. In addition, we found that only a quarter of the volunteers wanted to be involved in these changes. We consider the implications of these findings for improving support to volunteers experiencing change in their roles and responsibilities.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".