Findings from Innovate4Cities 2021 and Update to the Global Research and Action Agenda
Bibliographic record
Abstract
The Findings from Innovate4Cities 2021 and Update to the Global Research and Action Agenda details the research, policy, and public discussions key to advancing climate research and action in cities. This report documents key outcomes of the Innovate4Cities 2021 Conference (October 2021) and highlights current objectives and motivations, research gaps and priorities. Furthermore, this document presents a regional analysis of research and innovation needs falling under the original GRAA topical research areas and cross cutting issues (Edmonton 2018) and expanding to issues which emerged between the 2018 and 2021 conferences. This report has been developed by Global Covenant of Mayors for Climate & Energy (GCoM) and UN-Habitat based on the outcomes of 2021 Innovate4Cities Conference co-hosted by UN-Habitat, GCoM and co-sponsored by the Intergovernmental Panel on Climate Change. It is intended to inform research, policy and public discussions on the global research and action agenda for cities and climate change science. The authors have sought to ensure the accuracy of the material in this document, but they will not be liable for any ramifications incurred through the use of this report.
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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.175 | 0.087 |
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".