Reporting Volunteer Labour at the Organizational Level: A Study of
Bibliographic record
Abstract
Volunteer contributions in the production of services are an important resource internationally. However, few countries include volunteer contributions in their national accounts, even though many encourage their populations to engage in volunteering. At the organizational level, many nonprofit organizations using volunteers often limit their input to a footnote in annual reports acknowledging their contribution; few estimate their value in financial terms. As a result, their financial accounts lack information upon which to base decisions affecting the organizations and the communities they serve. Additional information is required to assess the impact of volunteers in individual nonprofits as well as the sector as a whole. This study focuses on Canada, one of the few countries that include volunteers in the national accounts, to examine to what extent nonprofit organizations estimate a financial value for these contributions and include this in their financial statements. This paper reports the results of an online survey of 661 nonprofits from across Canada. In order to understand why some organizations keep records for volunteer contributions and quantify them, two sets of explanatory factors are explored: organizational characteristics and the attitude of the executive director. We find larger organizations were more likely to engage in record keeping and estimating volunteer value, as were organizations with a relatively large group of volunteers and volunteer programs. The attitude of the
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".