Bibliographic record
Abstract
Despite its relatively small population size and limited resources, Toyama City, Japan has been successful in promoting its eco-policies. Because of its substantial work and efforts to address environmental and socio-economic issues, the city was designated as an eco-model city in 2008 and eco-future city in 2011 by the Japanese government as well as a model city of the United Nation’s Sustainable Energy for All Initiative in 2014 and the Rockefeller Foundation’s 100 Resilient City Initiative in 2014. Through literature reviews and interviews with personnel from the Environmental Policy section at Toyama City Hall, this article describes Toyama City’s eco-policies and examines how the city has succeeded in promoting eco-policies at both national and international levels. This study may provide other eco-cities with some insights to promote their eco-policies: Toyama City’s case can promote good practices and sound expertise for other cities around the world that have faced similar problems and share lessons learned.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".