Significant yet Unrecognized: The Informal Learning of Volunteers in Two Settings
Bibliographic record
Abstract
Abstract: This study examines connections between informal learning and volunteer work and values associated among two different groups of volunteers. Although there are many studies on voluntary work (Chinman & Wandersman, 1999; Cnaan, Handy, & Wadsworth, 1996; Hall et al, 2005), little is known yet about the extent, modes and effectiveness of volunteers ’ acquisition of new skills, knowledge, attitudes and values, and the relationship between formal, nonformal and informal learning in this process. We know from previous research, however, that there is a stronger association between community volunteer work time and community-related informal learning than between paid employment time and job-related informal learning (Livingstone, 1999). In exploring intersections between volunteering and learning, we were guided by the hypothesis that most volunteers learning is done informally, and that most of the resulting knowledge is tacit and thus difficult to articulate (Polyani, 1966). The “Informal learning of Volunteers ” is one of thirteen projects within the Canadian research network “Work and Lifelong Learning in the New Economy”2. Our project, one of two dealing with unpaid work, focuses on the learning processes and outcomes experienced by
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".