An Empirical Study on Campus Dwelling Environment Quality in Beijing and Its Influencing Factors
Bibliographic record
Abstract
Combining with the current dwelling environmental assessment system, this article reviews the domestic and foreign theoretical documents, and tries to construct an evaluation model based on the influencing factors of campus dwelling environment quality in Beijing, including natural landscape, amenities and cultural environment. The research indicates that the campus dwelling environment quality is linearly related with and can be effectively predicted by these three factors. It also shows that the regression coefficient of cultural environment is the highest among the three; but most interviewees didn’t appraise their campus dwelling environment quality high. Therefore, colleges in Beijing need to improve especially in the following three aspects – gas power system (natural landscape), population density (amenities) and manager quality (cultural environment) – to make the campus dwelling environment clean pleasant and eco-friendly. Key words : Universities in Beijing; Dwelling environment; Natural landscape; Amenities; Cultural environment
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".