Exploring the Dimensions of Self-Concept in Adolescents from Marginalized Communities
Bibliographic record
Abstract
Objective: This study aimed to explore the multidimensional nature of self-concept among adolescents living in marginalized communities, focusing on personal, social, and coping dimensions that shape identity development. Methods and Materials: A qualitative research design was employed using semi-structured interviews as the sole data collection method. Twenty-one adolescents aged 13 to 18 years from marginalized communities in Turkey were purposively selected to provide diverse perspectives. Interviews were conducted until theoretical saturation was reached, ensuring comprehensive exploration of emerging themes. All interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis with the aid of NVivo 14 software. Rigorous procedures, including iterative coding, peer debriefing, and continuous comparison, were applied to ensure credibility and trustworthiness of the findings. Findings: Three overarching categories of self-concept emerged: personal identity, social identity, and coping and resilience. Personal identity encompassed subthemes such as self-image, self-esteem, emotional awareness, autonomy, future aspirations, and self-reflection. Social identity included peer relationships, family roles, community belonging, cultural identity, social comparison, stigma and discrimination, and gender roles. Coping and resilience involved coping with marginalization, sources of support, coping strategies, resilience building, and identity negotiation. Participants described vulnerabilities including low self-esteem, stigma, and tension between traditional and modern cultural norms, alongside strengths such as supportive relationships, community pride, and resilience strategies. Illustrative quotations highlighted adolescents’ lived experiences and strategies for negotiating complex identities. Conclusion: The study underscores the multifaceted and context-dependent nature of adolescent self-concept, shaped by family, peers, community, culture, and broader structural realities. Despite significant challenges, adolescents demonstrated resilience and agency in redefining self-worth and negotiating cultural boundaries. These findings highlight the need for supportive interventions in schools, families, and communities that address both vulnerabilities and strengths, empowering adolescents in marginalized settings to construct positive self-concepts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".