The Theory of Constraints in Academia: It's Evolution, Influence, Controversies, and Lessons
Bibliographic record
Abstract
The ‗Theory of Constraints‘ (TOC)—more appropriately described as Management by Constraints (MBC)—is a case of a development that has raised an interesting debate in the field of Operations Management. Points of debate include how much of TOC is a ‗refocus‘, how effective it has been, and how it relates and compares to other developments in Operations Management. In this paper, we focus on what lessons academics may learn about disseminating controversial developments from the debate that has accompanied TOC. With the tremendous information explosion, we may see more such controversial developments. Therefore examining the case of TOC may help academics, the people who are expected to play an important role in dissemination, to deal with similar developments in the future, in a balanced and critical manner.
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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 | 0.065 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.011 | 0.105 |
| Scholarly communication | 0.033 | 0.036 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".