The Sustainable Development Goals and Your Community Foundation - Guidebook and Toolkit
Bibliographic record
Abstract
This Sustainable Development Goals (SDGs) Guidebook and Toolkit is meant for staff and board members of community foundations at all stages of engaging with the SDGs.The Guidebook and Toolkit is intended to meet community foundations where they are at, to provide practical examples, ideas and steps for aligning current community foundation work with the SDGs, and to provide next steps to deepen their impact through the SDGs. This document is divided into two sections. The first section is an SDG Guidebook. It will introduce the SDGs and provide global, national and local context for the Goals. It will explain why Community Foundations of Canada (CFC) and community foundations are well positioned to align with the SDGs and how the SDGs can deepen collective impact.The Guidebook includes:* An overview of the 2030 Agenda and the SDGs* How the global community came together to adopt the SDGs* Key concepts that underlie the SDG Framework and relevance to the work of community foundations* What CFC is doing to advance the SDGsThe second section of this document identifies practical approaches to align current work to the SDGs through an SDG Toolkit. In many cases, community foundations in Canada are already doing work towards meeting the SDGs, and the Toolkit is designed to show how to align current work with the SDG Framework.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.028 |
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".