Bibliographic record
Abstract
Toyota City has long been making efforts to promote its eco-policy based on five eco-themes: transportation, the urban center, industry, forests, and the public welfare and livelihood. A previous study that examined changes in citizens’ awareness regarding the city’s eco-policy between 2012 and 2015 illustrated that while the city has been successful overall to communicate its eco-policy to citizens, it has failed to do so in some eco-themes such as industry or forests. That is, despite some improvement, promoting the city’s eco-policy still remains an issue. This paper analyzes Toyota City’s eco-policy by using strategic marketing tools to help elaborate on effective eco-policy promotion in the framework of social marketing. Given that the ultimate goal of eco-policy is to promote citizens’ pro-environmental behaviors, the concept of social marketing is relevant here, as it includes the promotion of public policies, including eco-policies. Social marketing also may help understand why some cities succeed to promote their eco-policies while others fail to do so and elaborate on an effective policy-making and promotion. In this article, I will first describe a brief overview of Toyota City’s eco-policy. I will next employ different kinds of marketing approaches that may be relevant to promoting eco-policy. I will then employ several strategic tools (i.e., marketing mix, strategic purposes, PEST, strategic groups, and SWOT) to help analyze Toyota City’s eco-policy. This article ends with some discussions about how to promote the city’s eco-policy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".