Intellectual structure of information science 2011–2020: an author co-citation analysis
Bibliographic record
Abstract
Purpose This study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011–2020. It also examines changes in this structure from 2006–2010 through 2011–2015 to 2016–2020. Results will contribute to a better understanding of the information science research field. Design/methodology/approach The well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011–2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor. Findings The two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011–2015 but shared dominance with information behavior during 2016–2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016–2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011–2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016–2020. Originality/value Author co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.071 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".