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Record W2056364446 · doi:10.1287/inte.30.3.95.11655

Just-in-Time Manufacturing and Pollution Prevention Generate Mutual Benefits in the Furniture Industry

2000· article· en· W2056364446 on OpenAlex

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

VenueINFORMS Journal on Applied Analytics · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsWestern University
Fundersnot available
KeywordsBusinessControl (management)Pollution preventionPollutionProduction (economics)Investment (military)Work (physics)Environmental pollutionManufacturingEnvironmental economicsIndustrial organizationOperations managementMarketingEngineeringEnvironmental protectionEnvironmental scienceWaste managementEconomicsManagement

Abstract

fetched live from OpenAlex

Managing the natural environment is becoming increasingly important to manufacturing firms; yet managers are also being asked to simultaneously make changes to improve their firms' competitiveness. Just-in-time (JIT) manufacturing has long emphasized reducing waste. Similarly, pollution prevention stresses reducing pollutants before they are created. During field work in five furniture manufacturing firms and a subsequent survey in 1994, I observed links between investment in JIT and improved environmental performance. More surprising, I also found that an emphasis on pollution prevention, instead of pollution control, improved delivery performance. Thus, production and environmental managers should pursue JIT and pollution prevention as complementary initiatives that can improve performance along multiple dimensions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.220
Teacher spread0.207 · 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