The Churchill Fellowship to explore best practices in engaging and retaining students who are the first in their families to attend university
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.<br/><br/>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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it