Assessing the impact of a multinational corporation on local social capital : a case study of a semiconductor company in the Philippines
Bibliographic record
Abstract
This research explores how a multinational corporation (MNC) in the Philippines impacts on the social relations and integration of its host community in Baguio City. A lens of social capital is used to assess how the company influences local social patterns and networks, and in doing so, affects the community's capacity for development. Although emerging theories of development emphasize the importance of social linkages, little is known about how MNCs impact on these dynamics. Therefore, this study looks at the American subsidiary of Texas Instruments in the Philippines (TIPI) to determine whether it fosters relations within the local community that will support positive modes of development, or is shaping social networks that will stand to impede the community's achievement of its development goals. MNCs are first discussed generally in the context of globalization and the political economy of the Philippines. The paper then focuses on how this particular company operates in Baguio City. It is shown how, guided by principles of corporate social responsibility and contemporary ideologies of business, this widely accredited corporation builds community amongst its employees. How TIPI affects relations in the greater community of Baguio City is also explored and the implications for community development, as predicted by social capital theory, are discussed. In summary, it was found that while TIPI fosters social capital among its employees, it stands to inhibit social connections at a broader scope within the community. In that these community-wide networks are an important component of a community's development and planning capacity, the research suggests that TIPI, despite being socially responsible, does not represent a positive force for the community's development from the perspective of social capital. Several suggestions for policy and governance are provided by which the community might better ensure that MNCs foster positive development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".