Scientometric Analysis of Published Papers on Total Quality Management
Bibliographic record
Abstract
This research study is a Scientometric study of research publications on Total Quality Management (TQM) disciplines at Global perspectives from 2001 to2020. The paper summarises the global Research Trends in TQM from the analysis of the data. It uses different Bibliometric indicators in this context, and also applied the Growth of literature, Relative Quality Index RQI, Publication Efficiency Index, Absolute Citation Impact (ACI), and Relative Citation Impact (RCI)., and Quinquennial Publications. The results show that more research has been done in TQM. Additionally, it seems that the readability scores of publications have been growing up from 2001-2020. The total number of publications 89631 with 1635430 citations and ACPP 18.25% during the study. It is identified that a maximum number of 25921 (28.92%) research publications are contributed from the USA with 2425 High-Quality Papers. China, Canada, Italy, and France country show that more quality research output is being produced globally. The Quinquennial growth was 1.40 % for the Quinquennial period 2001-2004 to 2005-2008. 2005-2008, evidently, the contribution of publications in total quality management output was at a much higher rate in the early period of Block 2.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.151 | 0.203 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".