Citation classics published in <i>Knowledge Management</i> journals. Part II: studying research trends and discovering the Google Scholar Effect
Bibliographic record
Abstract
Purpose – The purpose of this study was to discover growing, stable and declining knowledge management (KM) research trends. Design/methodology/approach – Citations to 100 KM citation classics as identified by Serenko and Dumay (2015) were collected and analyzed for growing, stable and declining research trends. Findings – This research has two findings that were not theoretically expected. First, a majority of KM citation classics exhibit a bimodal citation distribution peak. Second, there are a growing number of citations for all research topics. These unexpected findings warranted further theoretical elaboration and empirical investigation. The analysis of erroneous citations and a five-year citation trend (2009 – 2013) reveals that the continuously growing volume of citations may result from what the authors call the Google Scholar Effect. Research limitations/implications – The results from this study open up two significant research opportunities. First, more research is needed to understand the impact Google Scholar is having on domains beyond KM. Second, more comprehensive research on the impact of erroneous citations is required because these have the most potential for damaging academic discourse and reputation. Practical implications – Researchers need to be aware of how technology is changing their profession and their citation behavior because of the pressure from the contemporary “publish or perish” environment, which prevents research from being state-of-the-art. Similarly, KM reviewers and editors need to be more aware of the pressure and prevalence of mis-citations and take action to raise awareness and to prevent mis-citations. Originality/value – This study is important from a scientometric research perspective as part of a growing research field using Google Scholar to measure the impact and power it has in influencing what gets cited and by whom.
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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.043 | 0.070 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".