The Ladder of Emotional Mapping: Visualizing Emotions for Planning Inclusive Cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it