Bibliographic record
Abstract
The achievements of ten African public rail companies are compared with each other using the multi-criteria methods ELECTRE I, PROMOTHEE II and JUDGES. The 25 first-level criteria retained are first grouped into 8 families, each capturing a second-level objective. The efficiency of the service is assessed by a technical-economic evaluation function grouping the first 4 families. Its efficiency is measured by an evaluation function involving the other 4 families made up of traditional financial ratios that are likely to capture the management balance and the origin of financial resources. The authors conduct longitudinal and cross-sectional analyses of the data by comparing the results generated by ELECTRE and PROMETHEE. The JUDGES software successively shows the tree of correlations observed between the 8 families of criteria, and the rank distributions of firms according to 8 families. From a methodological point of view, this work is a variant of ELECTRE I and checks the relevance of multi-criteria methods in terms of ranking the performance of public companies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".