A New Era in Social Service? Student Associational Culture and the Settlement Movement
Bibliographic record
Abstract
On his appointment as Bursar at Balliol College Oxford in 1883, Arthur Acland (1847–1926) noted that many of the undergraduates he met were “genuinely anxious to make themselves useful in work for the poor and others.” 1 Similarly at the more recently established women’s colleges, “we were all of us (or most of us) serious young women and thought we ought to do some good in the world” as Kathleen Courtney (1878–1974) later reflected. 2 From the 1880s students at universities and colleges in Britain and Ireland were increasingly receptive to the new ideas for practical social service that were being put forward by a range of writers and thinkers, and the universities emerged as important pools of volunteers for a range of new social institutions. The student social service movement drew on a varied set of intellectual and religious influences, streams that flowed together to raise the status of volunteering and make personal service at home or overseas incumbent on the educated classes. It is notable that significant developments did not begin until the expansion of higher education in the last quarter of the century, despite key advances in the theory and practice of charity in the late eighteenth and early nineteenth centuries. Through innovations like the creation of residential settlements in inner-city areas, British universities were central to what Clement Attlee (1883–1967) later described as a “new era in social service.” 3 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".