Thematic Categorization and Analysis of Peer Reviewed Articles in the LISA Database, 2004-2005
Bibliographic record
Abstract
A Review of: Gonzalez-Alcaide, Gregorio, Lourdes Castello-Cogolles, Carolina Navarro-Molina, et al. “Library and Information Science Research Areas: Analysis of Journal Articles in LISA.” Journal of the American Society for Information Science and Technology 59.1 (2008): 150-4. Objective – To provide an updated categorization of Library and Information Science (LIS) publications and to identify trends in LIS research. Design – Bibliometric study. Setting – The Library and Information Science Abstracts (LISA) database via the CSA Illumina interface. Subjects – 11,273 item records published from 2004-2005 and indexed in LISA. Methods – First, a search was set up to retrieve all records from 2004-2005, limited to peer review items (called “arbitrated works” by the authors (150)) and excluding book reviews. Second, thematic descriptor terms used for the records were identified. Frequency counts for descriptor term occurrence were compiled using Microsoft Access and Pajek software programs. From the results of this search, the top terms were analyzed using the Kamada-Kawai algorithm in order to eliminate descriptor term co-occurrence frequencies under 30. A cluster analysis was used to depict thematic foci for the remaining records, providing a co-word network that visually identified topic areas of most frequent publication. Conclusions were drawn from these findings, and recommendations for further research were provided. Main Results – The authors identified 18 “thematic research core fields” (152) clustered around three large categories, “World Wide Web”, “Education”, and “Libraries”, plus 12 additional peripheral categories, and provided a schematic of field interrelationships. Conclusion – Domains of greatest focus for research “continue to be of practical and applied nature,” (153) but include increased emphasis on the World Wide Web and communications technologies, as well as on user studies. A table of the most frequently occurring areas of research along with their top three descriptor terms is provided (Table 1, 152) (e.g., “World Wide Web” as the top area of research, with “online information retrieval” (268 occurrences), “searching” (132 occurrences), and “web sites” (115 occurrences)).
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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.188 | 0.214 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".