Exploring Life Quality Index via Education Level and Population Density
Bibliographic record
Abstract
The subjective nature of quality of life, makes it complex to measure the quality of life. Among them, finding a few key criteria that affect the quality of life in cities will be the main purpose. In this study, a linear regression model was used to predict the numerical relationship between population density, bachelor's degree rate and education Gini index, and further Akaike information criterion (AIC) and Bayesian information criterion (BIC) were selected to validate and adjust the model, and finally F-test was used to compare the initial model with the adjusted model. In addition, the random forest algorithm was used to classify the quality of life of each city by population density as well as education level to identify the common characteristics of cities with high physical quality of life index. Three classification decision trees created using different combinations of population density, bachelor's degree rate and education Gini index were used, followed by additional multiple decision trees generated by the random forest algorithm. The final results of the receiver operator characteristic curve (ROC) curves and confusion matrix of the two sets of decision trees were observed and evaluated to stratify the cities. The experimental results show that population density has a negative effect on quality of life, while bachelor's degree rate has a positive effect. Although the three indicators of population density, bachelor's degree rate and education Gini index cannot effectively distinguish the four levels, they can effectively distinguish which cities are not the first level. it shows that cities that achieve high education level and low population density are not necessarily the cities with the highest quality of life, but those that do not are definitely not.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".