National identity, state and social media discourse in Romania: the case of tennis
Bibliographic record
Abstract
During the Communist era, Romanian athletes won numerous medals across various sports, including tennis, and the Romanian public strongly identified with their success. However, in recent years, Romania has seen few notable achievements at major international events, with a few exceptions like Simona Halep, who won Grand Slam titles and was ranked World No. 1 in 2018. Additionally, Bianca Andreescu, a Canadian player of Romanian descent, won the US Open in 2019. In September 2019, Romanian journalist and public figure Cristian Tudor Popescu posted two short articles on his Facebook page questioning the relationship between sports, the state, and national identity. The posts generated 941 comments from his followers. Using thematic analysis, we gathered insights into the Romanian public’s current perspective on high-performance sports, represented here by Simona Halep’s achievements. The results suggested that, through sports, the public continues to express their national identity, while also voicing criticism of the system.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".