Going the distance: benefits and challenges of a long-term study of working-class, first-in-family university students
Bibliographic record
Abstract
This paper draws on experiences gained in a 16-year qualitative study of the experiences of working-class students who were the first in their family to attend university. Although the study suffered from attrition and analysis was complicated because it became increasingly difficult to understand the data through any one theoretical or conceptual framework, this complexity also offered the opportunity for a more nuanced approach, to become reassured in earlier interpretations, and to correct past misinterpretations. Their long-term commitment to the research project led many of the participants to articulate benefits they gained from being in the study for 16 years. The opportunity to tell their stories and have their experiences as first-in-family students validated was something they identified as especially important and valuable to them. QLR thus offers unique benefits that are not possible in cross-sectional research.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".