Collaborative public spaces and upgrading through global value chains: The case of Dongguan, China
Bibliographic record
Abstract
Abstract Research Summary How do multinational enterprises (MNEs) address host‐country challenges after the initial investment? And when does foreign direct investment (FDI) result in local upgrading? Using a study of FDI and global value chain participation in Dongguan, China, we find a mechanism in which FDI results in sustained local economic upgrading and improved MNE subsidiary performance: a collaborative public space (CPS). A CPS is a social space based on trust that enables different and divided actors to engage, sharing concerns and information in ways that they otherwise would be disinclined to consider. Using the CPS concept, we expand understanding of the effectiveness of MNE strategies in host‐country environments and the conditions in which FDI leads to change in global value chains. Managerial Summary The international strategy literature has found that FDI has ambiguous impacts on host‐country regions and firms, leading in some cases to upgrading or in others to local firm deskilling or decline. It has also shown that the postinvestment strategies of MNEs, particularly political connections or business associations, have mixed results. Using the concept of a CPS, we show how the construction of a trust‐based social space can improve MNE subsidiary and local firm performance and lead to changes in the structure and composition of global value chains (GVC). The GVC changes by incorporating new host‐country suppliers and buyers as well as increasing the value‐added, thus creating a new node and exchange patterns in the GVC.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".