The Churchill Fellowship to explore best practices in engaging and retaining students who are the first in their families to attend university
Bibliographic record
Abstract
This fellowship explored best practice in supporting and engaging students who are the first in their families to come to university. The terms first in family or first generation are used in this report interchangeably to identify students who are the first in their immediate family to participate in university, this includes parents, siblings, partners and children. This is a growing student population globally and one that is highly intersected by equity categories, such intersectionality impacting on student retention and completion. By investigating how institutions across the UK, Canada and the US consider these learners, the fellowship foregrounds innovative approaches and thinking in this regard. The fellowship enabled me to visit university sites across each of these locations and to both witness practical initiatives targeted at supporting this first in family (FiF) cohort and also, to have discussions with leading researchers and academics in the topic. The fellowship had a dual-fold focus seeking to explore innovative theoretical applications as well as investigate how various interventions are implemented.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".