Examining the Dynamics of Belonging and Alienation in Higher Education Through Photovoice
Bibliographic record
Abstract
The higher education system globally is inherently inequitable. Discriminatory practices and oppressive power dynamics are particularly prevalent in the South African higher education landscape, which is characterized by a legacy of colonialism and apartheid. As a result, although students from a wide range of backgrounds are increasingly participating in higher education, many students who do not fit the dominant status quo question their belonging within these spaces. Students' experiences of alienation within higher education can have profoundly negative physical, pyschosocial, and education outcomes. However, students also display agency in negotiating the exclusionary institutional cultures within their universities and succeed despite these experiences. Photovoice methodology can be a useful tool for critiquing and highlighting such agentic practices, and for foregrounding the voices of students. In this research brief, we reflect on two photovoice projects that sought to examine the complexity of students' experiences of belonging and alienation in higher education in South Africa. Our findings illustrate that although students may experience alienation on campus, they may also create spaces of belonging, "speak back" to, and challenge the exclusions inherent to campus life.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".