Breaking Barriers: Using Open Data to Strengthen Pathways in Community-Campus Engagement for Community Action on Environmental Sustainability
Bibliographic record
Abstract
The goal of this field report is to share learnings on productive dialogue between, and among, communities and campuses. Specifically, we will reflect on practical applications of co-creating a brokering tool to strengthen connections between local environmental non-profit organizations and the six postsecondary institutions in the National Capital Region (Ottawa/Gatineau). The report outlines a process of standardizing and visually depicting data on university and college engagement opportunities, created with an aim of making it easier for potential community partners, students, faculty, and even the general public to search, filter, and discover new programs, researchers, and services that match their interests.
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 | 0.898 | 0.518 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.575 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.000 | 0.646 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".