Upgrading in the Automotive Periphery: Turkey's Battery Electric Vehicle Maker Togg
Bibliographic record
Abstract
ABSTRACT Restructuring of the automotive industry in the post‐2000 period has led to the emergence of three strata of automotive manufacturing jurisdictions. Core automotive countries host the headquarters of global automakers. They retain most research and development (R&D) and high levels of production. By contrast, integrated peripheries offer low‐cost labour. While increasing levels of vehicle production have gravitated there, they have been unable to attract mandates for knowledge‐intensive portions of the automotive value chain. Finally, semi‐peripheries have neither a home‐grown automaker nor low‐cost labour. Consequently, they have been unable to gain mandates for R&D and struggle to maintain production. Thus, policy makers in non‐core countries consider a range of tools to either retain their position or ‘graduate’ from one category to another. Recently, the demand for battery electric vehicles (BEVs) has given rise to new vehicle manufacturers. Turkey is attempting to develop a BEV automaker and jump from an automotive integrated periphery country to one having a key attribute of an automotive core: a home‐grown automaker. This article reveals and discusses Turkey's generous incentives and assesses the challenges the Turkish BEV entrant will confront, as well as its potential to generate wider economic benefits. The authors also consider the application the Turkey case study has for our understanding of power and upgrading in automotive global value chains.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".