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Record W2339761750 · doi:10.5539/mas.v10n6p87

Financial and Support Policies Enabling Absorptive Capacity and Technology Acquisition in Iran’s Civil Aircraft Industry

2016· article· en· W2339761750 on OpenAlexvenueno aff
Mohammad Hossein Sabour, Mahdi Mohammadi, Erfan Khosravian

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAbsorptive capacityAnalytic hierarchy processInvestment (military)BusinessConstructivePoliticsGovernment (linguistics)Industrial policyFinanceIndustrial organizationProcess (computing)Economic policyEconomicsInternational tradeComputer science

Abstract

fetched live from OpenAlex

The aircraft industry has a significant role in country’s economic and sufficient investment in that, guarantees the long-term economic growth. This industry has facilitated the connection of local industry to global industry and will affect the country productive and economic performance. Due to the importance of aircraft industry, conducting various studies to design its development pattern seems essential to provide appropriate as well as efficient financial and support policies for absorptive capacity development of this industry. In the present study, first, those industrial patterns that are implemented in different countries have been examined in general. In addition, financial and support policies of different countries in aircraft industry are taken into consideration and the course of these supports have been drawn. Then, regarding the experiences of other countries as well as the economic, industrial and political conditions of Iran through field studies and interview with experts, the appropriate policies for constructive interaction between Iran’s civil aircraft industry and government are introduced and ranked by analytic hierarchy process (AHP) method.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.232
Teacher spread0.197 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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