Doing social constructivist research means making empathic and aesthetic connections with participants
Bibliographic record
Abstract
Social constructivist theorists tend to identify qualitative educational research as discovering meaning and understanding by the researcher's active involvement in the construction of meaning. Although these approaches have been widely influenced by Vygotsky's social constructivist approach, his own theoretical framework has received little attention in creating collaborative and dialectical relationship between the researcher and the research participants. The purpose of this article is to revisit Vygotsky's sociocultural theory of learning to reconsider ways how to conduct qualitative research through an emphasis on interactive and creative elements. The methodology applied in this narrative review article is based on qualitative, theoretical research. This article reviews Vygotsky's social constructivist approach, involving both primary and secondary sources. Bakhtin's (1990 Bakhtin, M. M. 1990. Art and answerability: Early philosophical essays by M. M. Bakhtin (M. Holquist & V. Liapunov, Eds.; V. Liapunov & K. Brostrom, Trans.). Austin, TX: University of Texas Press. [Google Scholar]) aesthetic relationship and Bresler's (2006a Bresler, L. 2006a. “Embodied Narrative Inquiry: A Methodology of Connection.” Research Studies in Music Education 27: 21–43. doi: 10.1177/1321103X060270010201[Crossref] , [Google Scholar],2006b Bresler, L. 2006b. “Toward Connectedness: Aesthetically Based Research.” Studies in Art Education 48 (1): 52–69.[Taylor & Francis Online] , [Google Scholar]) concept of aesthetics are also used to elaborate and extend Vygotsky's original work. From their shared concerns, two aspects of research are considered, research as: (1) a creative and transformative activity; and (2) as an affective, cognitive and embodied activity. Hence, authentic social constructivist research must deal with the empathic and aesthetic aspects of the researcher–participant relationship. Implications of these findings for researchers are also discussed.
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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 | 0.080 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".