Analysis of the evolution of aerospace manufacturing ecosystems
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.
Bibliographic record
Abstract
The aerospace manufacturing industry is predicted to continue growing. \nUnderstanding its evolution is thus essential to prepare optimal conditions to \nnurture its growth. This research aims to help the growth of emerging aerospace \necosystems by identifying evolution patterns and categorising key enablers that \nhave encouraged the growth of developed ones. The term aerospace ecosystem \nis used to embrace all the business activities and infrastructure that are related \nto the entire aerospace’s supply chain in a specific country. \nInspired by studies that have successfully combined economics and network \nscience, in this research, bipartite country-product networks are developed based \non trade data over 25 years. The United Kingdom (UK), the United States of \nAmerica, France, Germany, Canada and Brazil’s are first analysed as evidence \nsuggests that their aerospace ecosystems are within the most developed in the \nworld. Then, China and Mexico’s networks are analysed and compared with \ndeveloped ones, as these countries have evidenced emergent aerospace \necosystems. Results reveal that developed ecosystems tend to become more \nanalogous, as countries lean towards having a revealed comparative advantage \n(RCA) in the same group of products. Further analysis shows that manufactured \nproducts have a stronger correlation to an aerospace ecosystem than primary \nproducts; and in particular, the automotive sector shows the highest correlation \nwith positive aerospace sector evolution. \nKey enablers related to the growth of the UK and Mexico’s aerospace \necosystems are identified and categorised using interpretive structural modelling \n(ISM) and cross-impact matrix multiplication applied to classification (MICMAC) \nmethodologies. Results evidence relevant differences in the categorisation of key \nenablers among a developed and emergent aerospace ecosystems. On the other \nhand, it was identified that geopolitical factors and the automotive ecosystem are \nunderpinning enablers for both aerospace ecosystem’s evolution. \nThe final aim is that results of this research could be implemented on emerging \naerospace ecosystems by emulating the patterns and key enablers that have \ncharacterised the evolution of developed aerospace ecosystems.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it