An empirical investigation of the Malcolm Baldridge National Quality Award framework using causal Latent Semantic Analysis
Bibliographic record
Abstract
Numerous studies have investigated the linkages implied in the Malcolm Baldrige National Quality Award (MBNQA) framework. Those studies posited that the MBNQA quality experts defaulted to the premise that each construct is related to all others in the MBNQA framework because of the lack of specific knowledge about the causative relationships. Therefore, there is a need for both academicians and managers to explore the MBNQA framework as a non-recursive causal model as it was originally developed. This study uses a causal latent semantic analysis methodology to test the MBNQA as a non-recursive causal model using textual data obtained from scholarly MBNQA publications. Though the MBNQA framework is yet to be fully explored by both academicians and practitioners, this is the first study to show that the cumulative finding of prior research supports the contention that the constructs in the framework have substantial influence on each other.
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 | 0.040 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".