Celebrating Economies of Change: Brave Visions for Inclusive Futures
Bibliographic record
Abstract
This issue has been inspired by a path-breaking conference held by the Canadian Society for Ecologi-cal Economics (CANSEE), which took place this past May 2019 in Waterloo, Ontario. Entitled Engaging Economies of Change, the conference aimed to ex-pand existing research networks in the economy-environment nexus by building connections beyond the academy in order to meaningfully engage with the practicalities of building and implementing change. This issue captures the rich content shared during the event, as well as descriptions of the pro-cesses and efforts made to create a welcoming and respectful space where academics and community activists could build alliances and discuss common challenges. The conference organizers – all graduate students and activists themselves -- called this ‘building a brave space’.
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 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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.054 |
| Scholarly communication | 0.041 | 0.051 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".