The Impact of Gender Identity on In-Group Trust
Bibliographic record
Abstract
The article discusses the problem of the influence of gender identity on intra-group trust. Various scientific views on the category of trust are examined. Attention is also paid to the socio-psychological function of trust. Trust, in turn, is the foundation of the relationship between people and a factor in the effectiveness of cooperation. Having defined gender identity, we can say which form of identification positively affects internal group trust, and therefore the effectiveness of cooperation in a sports team. The article describes what type of gender identification (masculinity, femininity, androgyny) positively affects internal group trust, and does this have any connection with the success of a sports team. Also in the article to answer our tasks: 1) conduct a theoretical analysis of foreign and domestic theories of the formation of gender identity; 2) to study the theoretical foundations of the phenomenon of trust and analyze the importance of trust as a component of interpersonal communication in a sports team; 3) conduct a study to determine the gender identity of the person and determine the level of trust in the sports team; 4) analyze the relationship between gender identity and trust in a sports team; 5) to trace the influence of these factors on the success of a football team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".