The Position of a Teacher as a Factor of Forming Students’ Socio-Cultural Identities (On the Example of the Russian Civil Identity)
Bibliographic record
Abstract
The article presents experience of structuring and description of teachers’ position in the process of forming socio-cultural identity of the person, detailed in regard to the process of formation of one of the subtypes of socio-cultural identity–Russian civil identity. We identified and described real subjective, nominally subjective and non-subjective positions. Based on the author's vision of the essence of socio-cultural identity of the person, its relationship to personal and ego-identity, it is argued that educational organizations, teachers can play a significant role in shaping socio-cultural identity, and development of personal identity can be effectively accompanied. As invariant characteristics defining the teacher’s position as the subject of the formation of pupils’ socio-cultural identity we considered: level of information skills; importance of the teacher for students and/or “support” on the other significance; taking into account personal experience of pupils, their formed attitudes and preferences, referents’ influences on choices and self-determination; ability of the teacher to organize students’ activities and stimulate certain attitudinal reactions; the teacher’ reflection of his/her own characteristics of socio-cultural identity. For detailed descriptions, definitions of variant traits we considered the teacher’s position as the subject of forming the Russian civil identity and illustrated it with the results of empirical research conducted on the basis of schools in Voronezh and the Anninskiy district of the region, Voronezh State Pedagogical University.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".