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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.028 | 0.023 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; both teacher heads 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".