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Record W1607494574

Canada Furniture Outlook

2011· preprint· en· W1607494574 on OpenAlexaboutno aff
Alessandra Tracogna

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessOrder (exchange)Consumption (sociology)Production (economics)CommerceSupply and demandFurniture industryAttractivenessAgricultural economicsEconomyMarketingEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

Canada is an important player in the international furniture industry. It is a big and open market and a large producer, although slow growing. CSIL ranks the country as the 9th largest furniture producer and consumer at world level. In recent years the rapid growth of imports and the decline in exports have turned Canada into a net importer of furniture. Nevertheless Canada remains among the top ten exporters of furniture worldwide. This research is part of the Country Furniture Outlook Series, covering at present 70 countries. Each Country Furniture Outlook Report is structured as follows: Furniture sector performance is analyzed through updated furniture statistics and through tables, graphs and maps. Information covers all the main variables necessary to analyze the sector, from the supply side (Productive Factors, Furniture Production) to the market side (Demand Determinants, Furniture Consumption), also considering the international trading activity (Furniture Imports and Furniture Exports). Future sector prospects and CSIL's assessment of market potential are also provided (Furniture Market Potential) and cross-country comparison is also shown in order to enrich the analysis (Country Rankings). Short profiles of manufacturers and distributors operating in the country and a list of international Furniture Fairs, Furniture Association and Professional Furniture magazines are also provided. Background country information is also provided through summarized detailed statistics (Socio-Economic Data) as well as the country's attractiveness in terms of economic competitiveness (Business Climate).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
Threshold uncertainty score0.459

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.010
Science and technology studies0.0030.000
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1370.047

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 head0.076
GPT teacher head0.322
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2011
Admission routes1
Has abstractyes

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