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Record W4385951681 · doi:10.5152/forestist.2021.21021

An Analysis on the Competitiveness and Specialization Levels of the Countries in the Export of Wood and Articles of Wood

2022· article· en· W4385951681 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueForestist · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEconomic geographyInternational tradeAgricultural economicsEconomics

Abstract

fetched live from OpenAlex

The main aim of this study is to determine the export competitiveness and specialization levels of countries that export wood and wooden products. To do this, 10 countries with the highest export volume between 2010 and 2019 are determined under the HS-2007 product classification, using the “44-Wood and Articles of Wood” product group export data. The Relative Export Advantage (RXA) Index and Net Export Index (NEI) are used to measure export competitiveness. Moreover, for the product groups where countries gain competitive advantage, cross-country correlation is analyzed. With the analysis, efforts are made to determine whether there is a correlation between the countries’ specialization coefficient. On the one hand, it is seen that, among 21 product groups under the 44-Wood and Articles of Wood group, Poland has a competitive advantage in the export of 16 product groups, followed by Malaysia in 13, Austria and Vietnam in 12, and Canada and Indonesia in 11, respectively. Germany and USA, on the other hand, have competitive advantages in six product groups which make them the least advantageous among all these countries. Countries that have had a competitive advantage have usually shown specialization in exports as well with respect to their own trade performance. According to the results of the correlation test, which is between specialization coefficient within a certain product group, a strong correlation and a positive relationship are found between the countries that have a competitive advantage in exports. Especially in the product groups coded 4409 (wood, including strips and friezes for parquet flooring, not assembled), 4411 (fiberboard of wood), 4415 (packing cases, boxes, crates, drums, and similar packings, of wood), and 4418 (builders’ joinery and carpentry of wood), a high correlation exists between countries. To be specific, in the correlation test held for 10 countries and 21 product groups, Malaysia, matching in 40 categories with the other countries, has the greatest number of meaningful relationships. It is followed by Austria with 37 and Poland with 32. Therefore, it could be stated that this is a confirmation that these countries, in a high competition with the other countries, have a meaningful relationship in terms of specialization coefficient in the global market.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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