Exploring CSR PRactices of MNEs in Developing Countries
Bibliographic record
Abstract
Contemporary research within international business (IB) has substantially endorsed the co-evolution of institutions in supporting and sustaining multinational (MNE) activity.As research to date has largely ignored S U N D A Y AIB 2015 Conference Proceedings the subnational dimensions of this process, integrative insights from economic geography (EG) to IB may offer a potential avenue to enlighten our understanding of subnational institutional-MNE co-evolution.Drawing on an interdisciplinary perspective, this paper explores the mutual adaptation of subnational institutions with MNE investment in facilitating subnational institutional co-evolution.Our findings trace considerable changes amongst subnational institutions seeking to both engage with localized MNEs and enhance regional economic development.As such, the evidence suggests active participation and mutual exchange of subnational institutions with inward investment over time.Contributing to the strong body of co-evolutionary research, these findings utilize insights from EG to illuminate the localized activities of institutional-MNE co-evolution.(For more information, please
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".