Political agents or economic conditions: On factors of feminitives normalization in Western and Post-Soviet countries
Bibliographic record
Abstract
Introduction: Globalization is a global trend for most countries, despite their political, economic and cultural development. Globalization as a complex multifaceted phenomenon determines the image of the modern world. Through this process, domestic policy depends on globalization. This is also true for language policy of different countries which reflects the struggle for feminitives. Objectives: to identify the set of social and political factors contributing to the normalization of feminitives. Methods: cross-regional method, historical-genetic method. Results: the cases of six countries Western (France, Belgium and Canada) and post-Soviet (Russia, Ukraine, the Republic of Belarus) have been considered, the main factors contributing emerging disputes regarding feminitives, as well as their further normalization, have been identified. Conclusions: the agent factor in the form of feminist organizations and the economic factor (the level of economic development) in all the cases are considered a catalyst for emerging disputes regarding feminitives, as well as their normalization. Other factors are the women’s access to politics, a political regime that contributes or prevents the public discussion and normalization of feminitives, a geopolitical factor that is most typical for the post-Soviet countries, and a federal structure principle that has played a positive role in the Western countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".