Build the Volunteer, Build the Community: An Analysis of Sustainable Waterloo Region's Strategic Volunteer Program
Bibliographic record
Abstract
A lack of effective workforce development is a major challenge to the Canadian economy. Despite moderate increases in investment in employee learning since 2012, employer investment in workforce training is substantially less per employee today than it was in 1993 (Cotsman and Hall, 2017). This lack of investment in people hurts Canadian innovation and workforce productivity. Left unchecked, this gap leads to stagnating incomes for families and communities that get left behind in our competitive global economy. For individual workers it makes it harder for them to get on or move up their career ladder. Canada’s current labour market is highly competitive. Young workers and recent graduates today face high barriers to entering the professions they were trained for despite being the most educated generation in Canadian history. Companies often ask for previous work experience yet entry-level opportunities in many industries are tough to come by. Those in early career positions are often looking to move up in their organizations but have a hard time finding ways to exercise their skills and responsibilities in ways that will further their growth. Alongside the challenges faced by new entrants to the workforce, many nonprofit organizations lack the resources to hire paid staff to fulfill their mandates. Because of this many nonprofits rely extensively on volunteers. However, managing volunteers is time intensive for the staff that nonprofits do have and designing positions that allow volunteers to bring more than minimal skills to the table is difficult. Moreover, low-skilled volunteer positions are often a missed opportunity for volunteers to develop the skills and experiences that can directly benefit them. A deep integration of a volunteer-driven model with a nonprofit’s strategy is not to be taken lightly. In this report we refer frequently to a ‘strategic volunteer program’ because the necessary elements of the volunteer approach outlined here must be integrated into the entire strategic decision-making process to reap its full rewards. What is outlined here cannot simply rest in the hands of a volunteer coordinator – it has to be owned by the entirety of an organization’s senior leadership.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".