Economic Sentiment Changes in the Lubelskie Voivodeship in the 4th Quarter of 2011
Bibliographic record
Abstract
The Lubelskie Voivodeship (one of 16 Polish voievdeships) is situated in the south-eastern part of Poland in the interfluvial zone of the Vistula and Bug Rivers . On the west and north it borders on Mazowieckie Voivodeship, on the south on Podkarpackie, and on the south-west on Świetokrzyskie Voivodeship . On the east the Lublin region borders on Belorussia and Ukraine, creating at the same time the external border of Poland and the European Union . The location of the Lubelskie Voivodeship assures convenient room for economic growth on the capacious eastern markets . The Lublin region is located along the major transport routes linking the eastern and western part of the European continent . Across the area of the voivodeship run the shortest roads and railways through Berlin and Warsaw up to Minsk in Belorussia, Moscow in Russia, Luck, Kiev, Lviv and Odessa in Ukraine . Apart from that, the voivodeship is regarded as a peripheral region with a low level of economic development and capital expenditure attractiveness . The Lubelskie Voivodship’s area is 25 123 km2( 2), which makes up 8% of the country’s territory and places it in the third position among the Polish voivodeships . The Lubelskie Voivodeship belongs to the depopulated regions . At the end of 2010 Lubelskie Voi vodeship was inhabited by 2 151 009 people (5,6% of the citizens of Poland) . The region is characterized by a very low level of economic development . The expression of this is lower gross domestic product (GDP) per person comparing with the country’s average; in 2009 it was 67,2% the country’s average and was the lowest in Poland . At the same time the GDP per person created in the Lubelskie Voivodeship was only 37% of the EU average . The structure of GDP and a gross added value in Lubelskie Voivodeship is slightly different than in the rest of the country . First of all there is a great contribution of agriculture, hunting, fishing and forestry to the creating of gross added value . On the other hand the contribution of the industrial and commercial services related to finance, real estate and company’s services in creating GDP is much lower than the country’s average . The labour efficiency per worker measured by gross added value of production is considerably lower in general as well as in individual
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".