Understanding Experiences of First Generation University Students
Bibliographic record
Abstract
<JATS1:p>Over the past few decades universities have opened their doors to students whose parents and grandparents were historically excluded from societal participation in higher education for reasons associated with racial, ethnic, socio-economic and/or linguistic diversity. Many of these students are first generation - or first in their family to attend university (FIFU). While some progress has been made in responding to the needs of these internationally underserved learners, many challenges remain.</JATS1:p> <JATS1:p>This edited book features the unique and diverse experiences of first generation students as they transition into and engage with higher education whilst exploring ways in which universities might better serve these students. With reference to culturally responsive and sustaining research methodologies undertaken in Australia, Canada, New Zealand, South Africa, the UK and the USA, the contributors critically examine how these students demonstrate resilience within university, and ways in which success and challenges are articulated. Elements that are unique to context and shared across the international higher education milieu are explored. The book is replete with diverse student voices, and compelling implications for practice and future research</JATS1:p> <JATS1:p>The studies featured are centred on underlying theories of identity, intersectionality and barrier transcendence while valuing student voices and experiences. Throughout, the emphasis is on using strengths-based indigenous and decolonised methodologies. Through these culturally sustaining approaches, which include critical incident technique, participatory learning and action, talanoa and narrative inquiry, the book explores rich data on first generation student experiences at seven institutions in six countries across four continents</JATS1:p>
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".