Linear ordering of objects as applied to assesing economic activity of populations in voivodships
Why this work is in the frame
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Bibliographic record
Abstract
The aim of the paper is to compare the results of adopting different methods of linear ordering of objects applied to evaluating the level of economic activity of the population, as well as to select the method for the final assessment of the studied complex phenomenon. Two approaches have been presented. The first involves choosing from the results obtained by adopting all the analysed pattern and patternless methods, while the other proposes the choice of a method separately for each group. The studied problem has been demonstrated on the example of the level of economic activity of the population, which was defined on the basis of the data for the end of the first quarter of 2019, presented for voivodships and drawn from the Labour Force Survey in Poland (LFS). The analysis involved using several variants of patternless methods that differed from one another according to which formula of the diagnostic feature normalisation they used, as well as the following standard methods: Hellwig’s method, TOPSIS and the positional method based on Weber’s spatial median. In the group of the patternless methods of linear ordering, the one which yielded results closest to the results obtained using all the other variants was the method based on zeroed unitarisation. In the group of the pattern methods, similar conditions were met by Hellwig’s method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it