A Thematic Analysis of Interdisciplinary Journal of Information, Knowledge, and Management (IJIKM)
Bibliographic record
Abstract
Aim/Purpose: This study investigates the research profile of the papers published in Interdisciplinary Journal of Information, Knowledge, and Management (IJIKM) to provide silhouette information of the journal for the editorial team, researchers, and the audience of the journal. Background: Information and knowledge management is an interdisciplinary subject. IJIKM defines intersections of multiple disciplinary research communities for the interdisciplinary subject. Methodology: A quantitative study of categorical content analysis was used for a thematic analysis of IJIKM. One hundred fifty nine (159) papers published since the inauguration of the journal in 2006 were coded and analyzed. Contribution: The study provides synopsized information about the interdisciplinary research profile of IJIKM, and adds value to the literature of information and knowledge management. Findings: The analysis reveals that IJIKM disseminates research papers with a wide range of research themes. Among the research themes, Organizational issues of knowledge/information management, Knowledge management systems/tools, Information/knowledge sharing, Technology for knowledge/information management, Information/knowledge application represent the five main research streams of IJIKM. The total number of papers on organizational issues of knowledge/information management increased from 16% to 28% during the past 6 years. Statistical method was the most common research methodology, and summarization was the most common research design applied in the papers of IJIKM. The paper also presents other patterns of participant countries, keywords frequencies, and reference citations. Recommendations for Practitioners: Innovation is the key to information and knowledge management. Practitioners of information and knowledge management can share best practices with external sectors. Recommendation for Researchers: Researchers can identify opportunities of cross-disciplinary research projects that involve experts in business, education, government, healthcare, technology, and psychology to advance knowledge in information and knowledge management. Impact on Society: Information and knowledge management is still a developing field, and readers of this paper can gain more understanding of the dissemination of the literature of information and knowledge management involved in all relevant disciplines. Future Research: A longitudinal study could follow up in the future to provide updated and comparative information of the research profile of the journal.
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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 | 0.051 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".