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Record W7015067767

Review policies of catching up in emerging companies in aviation industry and present a pattern for Iranian aviation industry

2019· article· en· W7015067767 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAviationChinaAviation engineeringCommercial aviationAerospaceCivil aviationPortfolio
DOInot available

Abstract

fetched live from OpenAlex

The main purpose of this paper is to draw on the model of caching up in aviation industry with emphasis on passenger aircraft by using the experiences of late comer countries and considering the capabilities of the Islamic Republic of Iran, to present pattern and identify the strategies and measures needed in this direction. This is done by comparative exploration in aviation industry of countries such as China, Japan, Canada and Brazil, where the timeframe of their aviation development plans is nearer. The method of present study is qualitative and its strategy is multiple case study. In this research we are looking for methods of caching up the technology of the late comer in aviation industry. At first, historical of the aviation industry has been studied with a focus on the last 24 years. In this review, high citation paper even if related to older times, have been considered. we selected the countries such as Canada, Brazil, China and Japan, that are the owner of company such as Bombardier, Embraer in Canada and Brazil respectively, and aircraft models C919 and ARJ21 in China and the MRJ21 in Japan. By reviewing their capabilities actions of the China, Japan, Canada and Brazil obtained and compared with IRAN’S capabilities, Iran’s technology gap has been identified, and based on this, in catching up of technology based on three important factors: i) Indigenous R&D; ii) Export oriented and iii) Capability and capacity making, and the alignment pattern by emphasizing on part manufacturing and industrial offset with emphasizing on privatization and project implementation in six stages, are issued. After reviewing their historical development, the capability actions of the selected countries are obtained and compared with IRAN’S capabilities, and coming in the table 3 to 7. Iran’s technology gap has been identified in this area, and on this basis strategic implications in this area have been identified and present a pattern.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.014
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.286
GPT teacher head0.515
Teacher spread0.229 · 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 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
Published2019
Admission routes1
Has abstractyes

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