Bibliographic record
Abstract
Abstract This article is the first to explore the concept of ‘Citation classics’ in social policy by examining the articles published in five leading social policy journals that have 50 or more Web of Science (WoS) citations. It introduces the concept of citation analysis; discusses ‘citation classics’ in terms of definitions, measures, journals and databases; examines the literature on other social sciences, and particularly social work; and then focuses on the empirical material of citation classics in social policy journals. It finds 79 articles with 50 or more citations. Over half of the articles were written by authors based in the UK at the time of publication, with most of the others from the rest of Europe. About two‐thirds were classified as ‘conceptual’, and about a quarter were quantitative. Surprisingly few were qualitative or reviews. Roughly one‐third of articles were mainly focused on a particular service area, with the leading areas being employment, health, social care/community care or long‐term care. For the setting or focus of the study, nearly two‐thirds were comparative, while about a quarter were based on the UK. The leading topic was welfare regimes (14 articles). The limitations to this analysis include focusing on five social policy journals, and ignoring other outputs such as books; and the problem of determining what influence these articles have on the field of social policy. However, exploring the neglected area of citation classics in social policy provides one way of determining intellectual significance within the discipline.
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.
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 | 0.022 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.064 | 0.121 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".