Researching admissions: what can we learn about selections of applicants from findings about students in difficulty on a social work programme
Bibliographic record
Abstract
This paper explores the findings of a small‐scale empirical study of social work admissions data. It is designed to be exploratory in nature and used to illustrate key themes discussed in a previous paper. The study has been designed as a comparator to a Canadian study with the aim of building upon their findings within the current UK/England context. The research examines the relationship between applicants' pre‐admission information and their subsequent performance on the programme. Data relating to a sample of students and available at the pre‐admission stage (from application forms and interviewer report forms) and data relating to students on the programme who had been identified (by faculty) as having difficulties in one or more areas of their learning, are compared to a sample of those not identified as having experienced such problems. The two groups (total sample size=150) are drawn from one university in southern England and comprise students from both undergraduate and postgraduate programmes. Methodological issues are critically analysed and findings are explored and compared to that of the Canadian study. Key themes regarding the seemingly complex relationships between performance on the programme and academic background, extent of previous experience and a range of other factors are discussed and examined in relation to other available literature. Implications are explored in relation to current practice and development needs within social work education.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".