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Record W2603226531

Top 200 furniture manufacturers worldwide

2017· preprint· en· W2603226531 on OpenAlexaboutno aff
Cecilia Pisa

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessRevenueProduct (mathematics)ChinaConsumption (sociology)Distribution (mathematics)CommerceListing (finance)Agricultural economicsMarketingFinanceGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The Top 200 furniture manufacturers worldwide report provides an overview of the furniture competitive system, selecting and listing 200 furniture top companies by their revenues, products and geographical coverage. Data on global furniture consumption, trade and production are included for time frame 2011-2016. Comparisons between industry trends and top furniture manufacturers are provided. Top 200 manufacturers are broken down and analysed according to headquarters location, activity and furniture product specialization (office, kitchen, home and upholstered furniture) as well as financial performance (turnover and employment). Profiles are structured with the following information: Companies directory (headquarters address, telephone number, website, email address, year of establishment), Business activity (product portfolio, furniture product specialization), Financial performance (total revenues and number of employees), Manufacturing activity (facilities location, plant expansion and cost rationalization strategies), Sales breakdown by product and by geographical area, Distribution activity: brands, retailing and branding strategies for the domestic and international markets. Companies selected have headquarters in the following countries: Australia, Austria, Azerbaijan, Brazil, Canada, China, Denmark, Finland, France, Germany, Hong Kong, India, Italy, Japan, Liechtenstein, Lithuania, Malaysia, Netherlands, Norway, Poland, Romania, Russian Federation, Singapore, South Africa, South Korea, Sweden, Switzerland, Taiwan, Turkey, United Kingdom, USA.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.387
Teacher spread0.329 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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