Infiltration, interdiction, and other covert supply chain operations: a research agenda
Bibliographic record
Abstract
Purpose The masterminds behind covert supply chain operations aim to hide their activities from government agencies and society at large, often for illegal gains or to intentionally cause harm. This conceptual article outlines a research agenda for future studies by framing covert supply chain activities and the countermeasures used to disrupt them. Design/methodology/approach Secondary data were collected from various news sources (observation) and analyzed to understand the nature of covert supply chain operations and efforts to identify and disrupt them (conceptualization). Findings To date, covert supply chain operations and counter-operations categories have been scarcely scrutinized in the supply chain literature, and our framework presents many fruitful avenues for further research. Practical implications Policymakers may aim to enhance the visibility of covert supply chains to achieve strategic objectives. Our framework enables logistics providers, network orchestrators, and shippers to identify vulnerabilities and detect covert infiltration by hostile actors within customer supply networks. Originality/value The mainstream supply chain literature has viewed supply chains of illegal goods and disruptive counter-operations as piecemeal. This conceptual article addresses the topic holistically to create a framework for guiding future research.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".