The house of cards: equity-group students’ experiences of structural inequity in higher education
Bibliographic record
Abstract
Globally, correlations are reported between lower academic attainment and university students being a member of an ‘equity’ or ‘historically under-represented’ group. We seek to illuminate how this so-called awarding gap might transpire. Taking a longitudinal qualitative approach, 35 undergraduate students from equity and diverse backgrounds at an Australian university participated in a series of interviews. Using a metaphorical framing, we liken the students’ participation in higher education to building a house of cards, which refers to studying within a precarious, unstable or fragile situation. Students learn to work with the cards they are dealt through much scheduling, ordering and invisible work. Inevitably, when one of the cards wobbles and impacts their study, a partial or complete collapse of the structure can swiftly occur. In the aftermath, students try to rebuild. The house of cards metaphor illustrates how universities reproduce disadvantage through the institutional structural barriers that interact with individual students’ lives. Of concern, students perceive participation challenges to result from their individual faults, instead of from precarious structural foundations on which their house of cards is built. This article makes a contribution to critically interrogating structural inequities that influence students’ participation and, ultimately, success in higher education.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".