CICP Data Literacy 1.0 - Module 1: Data in the Nonprofit Sector
Bibliographic record
Abstract
The Charity Insights Canada Project’s (CICP) Community Education Centre (CEC) presents "Data Literacy 1.0," a comprehensive plan aimed at empowering individuals in Canada's nonprofit and charitable sectors with essential data literacy skills. The ability to effectively utilize data has become crucial for organizations to achieve their missions and create significant impact. This series of CEC modules is a collaborative effort to bridge the gap between data and actionable insights, providing the necessary knowledge and tools for participants at any stage of their data literacy journey. Module 1: Data in the Nonprofit Sector, serves as a foundational course, consisting of six detailed capsules that cover a spectrum of data-related topics in the nonprofit context. From data collection and sources to analysis and interpretation, each capsule is designed to not only impart knowledge but also to encourage critical thinking and practical application. This module aims to empower nonprofit professionals to use data effectively, fostering informed decision-making and driving transformative change within their organizations.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.026 |
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".