Le concept de perezhivanie pour étudier la complexité des interactions entre l'enfant et l'environnement socioculturel
Bibliographic record
Abstract
Interacting with children in their make-believe play to promote their learning and development is a complex and sensitive teaching practice (Wood, 2007). It raises many questions for early childhood teachers (Bouchard et al., 2021; Pyle and Alaca, 2018), including maintaining the child's perspective during these interactions (Clerc-Georgy et al., 2021). An original way of finding answers to these questions would be to involve a concept from the cultural-historical approach, namely perezhivanie. The aim of this theoretical article is to present perezhivanie in the context of Vygotski's writings and to explain the relevance of its use in educational research. In his early writings, Vygotski (1994) gives a phenomenological meaning to perezhivanie. It refers to the consideration of any lived experience and how it is lived by intertwining affective and cognitive dimensions (Veresov, 2017). With the advancement of his cultural-historical theory, Vygotski circumscribed perezhivanie as a theoretical concept (Veresov, 2016). From this perspective, the concept of perezhivanie allows us to understand the influence of the environment on the child's developmental process (Vygotski, 1994). Although fraught with theoretical and methodological challenges (Brennan, 2014), perezhivanie is a relevant framework to consider for future educational research, since it would make it possible, in particular, to study the dynamics of the interactions in the early childhood education classroom, such as between the teacher and the child in a make-believe play situation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".