PROFILING: A STRATEGY FOR SUCCESSFUL VOLUNTEER RECRUITMENT IN CREDIT UNIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Credit unions are non‐profit financial organisations that provide financial services to their members. They are located in 97 countries across the world. All credit unions are governed by a volunteer board and many are reliant on volunteers for all their labour requirements. However, recruiting volunteers is a problem. The literature on recruitment issues in volunteering in general, suggests that the not‐for‐profit sector looks to the private sector for guidance on recruitment policies and approaches. One such approach which is considered in this paper is ‘market segmentation’ wherein the potential volunteer body is profiled to determine if an individual is likely to volunteer and if they are, to identify the type of role they are most likely to be attracted to. Prior literature on volunteering in non‐profit organisations suggests that certain types of individual (dominant individuals) are more likely to volunteer. This paper investigates whether this dominant status profile is evident amongst volunteers in credit unions in Northern Ireland (NI). The study finds that people with dominant characteristics are more likely to be attracted to volunteering to the board of directors and individuals who have less dominant traits overall should be offered more social/participative type roles. This information can be used by credit union governing boards for volunteer recruitment, retention and management purposes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it