SCIENTIFIC LIFE: 4TH INTERNATIONAL CONFERENCE ON CANADIAN, CHINESE AND AFRICAN SUSTAINABLE URBANIZATION (ICCCASU4), VIRTUAL CONFERENCE MONTREAL, CANADA, 28 - 31 JULY 2021
Bibliographic record
Abstract
With a focus on urban world from regions which account more than 2,7 billion of the world population, The International Conference on Canadian, Chinese and African Sustainable Urbanization (ICCCASU) is an important international think-tank which promotes “sustainable and inclusive urban development in a forum based on the diverse but complementary experiences of Canada, China, and African nations” (ICCCASU, 2021). ICCCASU take place every two years and rotates between the three regions, since its inception in 2015. Originally it was a collaboration between UN-Habitat and the University of Ottawa; then ICCCASU has expanded to a consortium of three Canadian universities (Carleton University, McGill University, and University of Ottawa) and Chinese and African universities. The 2121 conference (ICCCASU4), with more than 200 participants from 39 countries, was organized virtually by David Covo, McGill University, and Tonton Mundele, Global Affairs Canada, under the theme of “Density, Diversity, and Mobility: The City in an Era of Cascading Risks”.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.160 | 0.033 |
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".