MétaCan
Menu
Back to cohort
Record W4409832537 · doi:10.1080/21681015.2025.2483755

Coordination contracts for a dual-channel green supply chain with demand disruption

2025· article· en· W4409832537 on OpenAlex
Mohammad Yavari, Kavian Rahmani, Armin Jabbarzadeh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Industrial and Production Engineering · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsDual (grammatical number)Channel coordinationBusinessSupply chainChannel (broadcasting)Industrial organizationOperations managementSupply chain managementComputer scienceComputer networkEconomicsMarketing

Abstract

fetched live from OpenAlex

The primary objective of this research is to tackle the coordination issue in a green dual-channel supply chain under demand disruption. In the investigated supply chain, one kind of green product is being produced and sold through a dual-channel distribution system. To achieve coordination in the green dual-channel supply chain, three types of contracts are proposed. This study’s applications extend to both centralized and decentralized green supply chains, addressing the effects of demand disruption on coordination contracts. It also provides insights into the necessary contract modifications to enable coordination in disrupted supply chains. Results indicate that the profit-sharing and two-part tariff contracts not only can effectively coordinate the supply chain under demand disruption but also provide a win–win situation for both the environment and the supply chain members. The revenue-sharing contract, on the other hand, shows poor effectiveness and does not have enough desirability to be embraced by the members.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.208
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it