RESULTS OF LOCAL ELECTIONS IN HARGHITA COUNTY, SEPTEMBER 27, 2020
Bibliographic record
Abstract
The results of the local elections in Harghita County, 27th September 2020. This study aims to faithfully depict the territorial distribution of the electoral decisions taken by the inhabitants of Harghita County at the local elections held on 27th September 2020. Based on official statistical data from the Central Electoral Office (https://locale2020.bec.ro/rezultate), the voting for mayors, local and county councilors, as well as president of Harghita County Council was thoroughly examined. 67 mayors were elected in the 21st Electoral District ‒ Harghita County, their political affiliation being as follows: DUHR (48), SDP (7), HAT (5), IC (5), FHP (1) and NLP (1). Of the 835 local councilor seats, most were won by DUHR (570), followed by HAT (138), SDP (49), NLP (34), FHP (14), CI (13), PMP (8), SRU-LUSP (3), PRO ROMÂNIA (2), while the Magyar Civic Party, National Unity Block, Humanist Power Party (Social-Liberal) and the Liberal and Democrat Alliance won a single seat each. Harghita County Council is comprised of 30 county councilors (with one less seat than in the 2016-2020 legislature), 19 being DUHR members, 4 HAT, 3 SDP, 2 FHP and 2 NLP. Borboly Csaba, the DUHR candidate, received a total of 69 822 votes (64.54% of the votes), thus winning his fourth term as president of Harghita County Council.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".