Citation classics published in <i>Knowledge Management</i> journals. Part II: studying research trends and discovering the Google Scholar Effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it