The Ladder of Emotional Mapping: Visualizing Emotions for Planning Inclusive Cities
Bibliographic record
Abstract
Many countries in the modern era strive to keep up with the world's rapid development in many economic, environmental, and social aspects, particularly on the urban scale and city planning, as well as competition for access to the highest levels of luxury in terms of buildings, designs, and iconic buildings that distinguish each country in the media from its counterparts from neighboring countries. In the region, and possibly internationally. Some countries were forced to relocate a number of their cities and capitals, as well as develop new alternatives for them in new places. In the context of implementing these strategies, decision-makers overlook the social and emotional dimensions of citizens, making it difficult for planners and those involved in the design process to understand the human requirements and needs of the user, resulting in the neglect of many aspects that citizens require, such as the design of the urban environment, planning of public areas, and green open spaces. This paper aims to highlight the importance of taking the emotional side of the user into consideration and integrating them into the decision-making process through participatory planning to develop decision-making strategies that include the preferences of all stakeholders in the planning process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".