Exploring the Applications & Challenges of Data Analytics in Non-Profit Organizations
Bibliographic record
Abstract
In recent years, the use of data analytics is widely documented for its use in for-profit businesses. So too have risen open data initiatives, calls for the ethical use of data, and its use for the greater good. At the conflux of these calls are the use of data analytics in non-profit organizations. In consideration of the benefit data analytics would bring to non-profit organizations, our exploratory study strives to understand the extent to which data analytics are being applied in non-profit organizations, including use cases and challenges experienced. For this study, we recruited 14 participants representing employees, volunteers, and consultants in non-profit organizations in the city of Ottawa, Canada. Our findings indicate that data analytics are being utilized on varying scales and predominantly in the areas of fundraising, program evaluation and marketing. Multiple challenges, including with the data itself, knowledge requirements and time limitations are identified as inhibitors.
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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 | 0.045 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".