Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the journal bibliometric characteristics of Collection Building and the subject relationship with other disciplines by citation analysis. Design/methodology/approach This study explores the distribution of articles and subjects of references and analyses the various aspects of Collection Building from 2005‐2012. There are 179 articles in Collection Building in eight selected years. In total, 32 issues pertaining to eight volumes of Collection Building were consulted and relevant details of the citations at the end of each article were noted on an excel sheet. The recorded data were analysed, interpreted and tabulated. Findings The results of this study revealed that 179 articles were consulted from eight volumes (2005‐2012) which carried 2,388 citations including 85 self‐citations. The majority of articles (30.17 per cent) recorded between 10‐19 range of citations per article followed by (28.50 per cent) 1‐9 range. The majority of articles were contributed by single authors (65.92 per cent) and majority of contributors were from the USA (69.96 per cent) followed by Canada (3.95 per cent) and India (3.95 per cent) respectively. Journal articles (42.71 per cent) were the most cited source materials, followed by online and electronic sources (25.80 per cent), books including edited books (20.44 per cent), newspapers (5.23 per cent) and so on. Out of 179 articles, tje majority of articles (33.52 per cent) were Research papers followed by Case study (30.73 per cent), Literature review (12.85 per cent) and so on. The majority of articles (66.48 per cent) were recorded between 6‐10 pages, followed by 25.70 per cent articles between 1‐5 pages. Out of 1,020 journal articles, Collection Building (9.02 per cent) was the top ranked journal, followed by The Journal of Academic Librarianship (5.0 per cent) and College & Research Libraries (4.22 per cent). Research limitations/implications Research was limited to the journal entitled Collection Building during eight years (2005‐2012). In total, 32 issues and 179 articles were covered by the study. Originality/value The outcome of the study is an original research work with citation analysis of Collection Building. It highlights the study of 179 articles of Collection Building in various ways.
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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.059 | 0.104 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".