Tracking progress towards sustainable development in cities
Bibliographic record
Abstract
As the implementation of the sustainable development goals (SDGs) gains momentum, cities and communities are at the forefront of delivering change. These efforts, known as localizing the SDGs, require communities to embrace the challenge of measuring progress, requiring data management and communication. In the face of a growing movement to localize the SDGs, the International Institute for Sustainable Development (IISD), an independent think tank, recognized that its experiences with Peg, a community indicator system (CIS) for Winnipeg, Manitoba, could assist other communities’ efforts to measure local progress towards the SDGs. In this chapter, we share the experiences of building and refining Peg and its indicators. Peg was founded in 2009 as a partnership between the International Institute for Sustainable Development and the United Way of Winnipeg (UWW). Like many other CIS projects, Peg faced significant hurdles in ensuring its own sustainability, including funding, access to data, and providing meaningful measurements that matter to Winnipeggers. In 2017, we undertook a substantial redesign of Peg to align its indicators with the SDGs. The following sections outline the lessons learned during Peg’s development and redesign to localize the SDGs as metrics of performance for sustainability outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".