Linear ordering of objects as applied to assesing economic activity of populations in voivodships
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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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".