Bibliographic record
Abstract
Since 2009, the International Institute for Sustainable Development (IISD), in partnership with the United Way Winnipeg (UWW) in Manitoba, Canada, has been managing Peg, an online community indicator system (CIS) that tracks the well-being of Winnipeggers. While Peg’s first online platform was technologically advanced for its time, the original site became increasingly burdensome and costly to manage as it aged. In 2018, the IISD undertook an overhaul of the platform used to run Peg. As the IISD embarked on developing a new platform for Peg, it became apparent that many other communities and governments were facing similar difficulties collecting, managing, and communicating their data online as it can be difficult and expensive especially for small and mid-sized cities with few affordable and tailored resources. In response, instead of developing a platform just for Peg, the IISD developed an open-source tool, known as Tracking-Progress, that could be used by communities of any size. This chapter explores how the IISD developed Tracking-Progress based on its experiences with Peg, provides insight into how a growing network of platforms is expanding the tool’s functionality, and shares how the Tracking-Progress tool is providing communities globally with the resources required to localize the United Nation’s sustainable development goals (SDGs).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".