Exploring definitions of social justice : a qualitative study of moral dialogues with university students
Bibliographic record
Abstract
This qualitative research built upon a sociocultural approach to moral functioning to examine how six university students living in Vancouver defined social justice. Two research questions guided this study: 1) How do participants define social justice? and 2) How do they perceive their definitions of social justice are informed by their cultural background? Six semi-structured interviews were conducted to collect the data, during which the participants engaged in moral narratives that drew upon their past experiences, events from their sociocultural contexts and a fictitious narrative provided by the researcher. Through moral narratives, therefore, the participants crafted their definitions of social justice, defined as conceptual systems mediating their moral actions (e.g., reflection, dialogue, imagination, and creativity, among others). Braun and Clarke’s (2006) thematic analysis, three main themes were identified across the data: 1) equality and non-discrimination as core aspects defining social justice, 2) pathways from social injustice toward social justice, and 3) authoring themselves through moral dialogues. Participants not only defined social justice but expanded their inner moral dialogue, allowing them to reconstruct their past experiences and imagine possible social justice futures. These findings are potentially relevant: 1) to the literature on moral development; and 2) to intercultural educational curricula and pedagogy.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.036 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| 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".