تحلیل تاریخی زیرساختهای قابلیت ساز درون بنگاهی در صنعت ساخت هواپیمای مسافری (بررسی موردی: امبرائر برزیل، بمباردیر کانادا و پروژه های ساخت هواپیمای مسافری در چین، ژاپن و ایران)
Bibliographic record
Abstract
This study seeks to identify intra-firm capability infrastructures by examining the historical trends of countries: Canada, Brazil, China, and Japan. As we look at the historical trajectory of aircraft technology acquisition in these countries and their entry into this complex field, it is clear that each of these countries first reached a threshold level of capability. In other words, they created the development capacity in their country. It was necessary to develop the capacity to build capability infrastructures. By examining the historical course of capability building in these countries and comparing them with Iran''''s capabilities in this area and analyzing them, Iran''''s technology gap with the target countries was identified. And finally, by aligning and comparing the historical trends of these countries, the necessary infrastructure at the enterprise level has been formulated as a prelude to technology catching up. It should be noted that the spectrum capability infrastructures are an extended spectrum, but we focused on firm-level capabilities in this spectrum, and filling them all is beyond the capacity of a single paper.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.020 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".