MétaCan
Menu
Back to cohort

The Steel Industry in Global Competition and Limited Capital: A Case Study of the Steel Industry in Bolivia

2023· article· en· W4322766614 on OpenAlexaboutno aff
Antonio Rafael Da Riga

Bibliographic record

VenueINFLUENCE International Journal of Science Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Tax incentiveInvestment (military)LegislationIncentiveOrder (exchange)BusinessGovernment (linguistics)Competition (biology)Capital (architecture)Value-added taxTax reformEconomicsEconomic policyInternational economicsPublic economicsMarket economyFinanceGeography

Abstract

fetched live from OpenAlex

This article has as its objective to present a study of the impact of tax costs of the investment destined to the expansion of a metallurgical plant in Bolivia, compared with the tax load that would occur if such the investment were made in other countries, specifically, the USA, Canada and Chile. The study has been developed within the context of recognition that certain events which provoke effects upon the level of competitiveness of companies are beyond their action range. Among such events, the tax load, an integral element of government macroeconomic policies, has been considered in this study. The multiple case study method was used to measure the tax cost of a planned investment, contemplating the effective legislation in each of the examined countries. Results of the study allow concluding that, among the four countries under analysis, Brazil presents the largest tax cost, significantly greater than the other countries, which offer fiscal incentives, including a negative tax load. Among the remaining three countries, the company competitiveness has been favoured according to the following order: by Chile, the USA and Canada.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.315
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueINFLUENCE International Journal of Science ReviewSame topicGlobal Trade and CompetitivenessFrench-language works237,207