HallMeasurement Issues Measurement Issues in Surveys of Giving and Volunteering and Strategies Applied in the Design of Canada’s National Survey of Giving, Volunteering and Participating
Bibliographic record
Abstract
Despite the frequent use of surveys to study charitable giving and volunteering, little is known about the quality of the data collected. This article discusses the challenges associ-ated with designing giving and volunteering surveys, suggests strategies for improving measures of giving and volunteering, and shows how they were applied in the design of Canada’s 1997 National Survey of Giving, Volunteering and Participating. Surveys are frequently used to collect data about giving and volunteering; however, the quality of the data is seldom known, and the measurement chal-lenges inherent in such surveys are not well recognized. For example, the inability of respondents to accurately recall past behaviors introduces both random errors and probable downward biases to estimates of giving and vol-unteering. On the other hand, the tendency of some respondents to provide socially desirable responses may lead to an overreporting of giving and volun-teering. The extent to which these two potential sources of bias may offset one another is seldom known and varies from survey to survey according to the particular way in which giving and volunteering are measured. Such threats
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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 | 0.494 | 0.581 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".