Bibliographic record
Abstract
The field of supply chain management (SCM) has experienced radical changes in its short life period. Originating from 1980 and 1990s management trends and techniques of operations management, passing process oriented and system oriented approaches in the 1990s, and now attracting the attention to behavioral approaches have all caused SCM to be largely vertiginous. So dealing with its future requires a more accurate method than common predicator fashions. Therefore, the chapter first considers SCM as a body of knowledge in which evolution is based on its theoretical foundation, and therefore, prevalent research paradigm(s), research methodological base(s) used by developers, and also real world challenges that motivate it. Consequently, the authors review current status of SCM from standpoint of the discipline’s theory, its conceptualization process, and most used research methods and approaches. Then the authors will be able to use its implications to adopt an appropriate model of philosophy of knowledge for scientific change and knowledge growth of SCM. This can be used as a guide to the future of supply chain management.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".