Geographical Variation of the Quality of the School Offer in the City of Mbujimayi (Dr Congo): Data Processing by the Method of Multivariate Spatial Analysis
Bibliographic record
Abstract
This article presents spatial data related to the quality of school infrastructures in the city of Mbujimayi so as to bring out their characteristics and the typology of quarters which contain these infrastructures. The variables used are presented in the spatial information matrix. The principal components analysis and factorial analysis of correspondences helped make the description of associations based on these variables. The correlation matrix gave birth to channels of the strongest positive correlations (r ≥ 0.60) and resulted in the principal component analysis. The main results are:The cartography of quarters containing schools.The spatial disparity between the variables having degrees of affinity with the location and topography of adequate school site, sanitation, access to public services, and matching buildings. These variables are opposite to those related to unsanitary, localization and indecent topography of the site as well as non-school access to public services.A strong interaction between sanitation and access to public services, including running water and electricity, determining the quality of schools.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".