Bibliographic record
Abstract
The use of flexible methods in social work allows social workers to be more flexible, client-oriented, adaptive and responsive in a dynamic environment, respond to changes faster, achieve better social impact results, and be more coordinated in cooperation with other professionals. The article demonstrates the results of descriptive bibliometric analysis and scientific mapping (using the Biblioshiny software) of more than 750 articles and monographs indexed by Scopus. Since the appearance of the first study in 1969 and until 2000, this topic was almost not the focus of scientists; the year 2000 was determined using Reference Publication Year Spectroscopy as the key date of interest growth (the year preceding the appearance of the Manifesto for Agile Software development), since 2001, the number of publications on this topic has grown exponentially. By a Sankey plot, interdependence between top references, top authors and top keywords was summarized. According to Bradford’s law, scientific journals are structured according to the contribution to the dissemination of knowledge in the subject area. Scientists from the USA, Great Britain, China, Australia and Canada have scientific leadership in this field. The TOP-10 global and local cited documents were analyzed in detail, and “occasional” and “sore” authors were distinguished according to Lotka’s law. The most popular thematic research areas on applying flexible methods in social work are presented in visual design as a word cloud (tag cloud, weighted list) and a treemap. The analysis proved that keywords across various clusters and research sub-themes are closely interconnected. The most relevant and advanced research categories were identified by analyzing the increase in relevance and the level of subject development, as well as their trends over time. A trend toward convergence in scientific research thematic progression in scholarly literature was explored using an alluvial diagram (a longitudinal thematic map). Constructed maps of relevance degree and development degree of subtopic in documents with a focus on agile or adaptive social work methods made it possible to determine niche, emerging, and declining topics.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.059 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".