A Cross-national Study of Attitudes and Group Labeling: Multinational Corporation (MNC) Workers in Canada, Brazil, and West Germany
Bibliographic record
Abstract
Many studies concerning multinational corporations (MNCs) are replete with theoretical models and case studies that treat MNCs as stand-alone entities. Very little time and effort is given to understanding the context in which MNCs operate. This context includes not only the fact that MNCs transcend national boundaries (political as well as geographical), but also the meaning of work and being part of a multinational work force for those employed within MNCs. This thesis is an effort to elucidate how the political/societal/cultural contexts of different host countries affect the attitudes of those workers most directly involved with foreign-owned MNCs. By shifting the focus from the MNC to the political/societal/cultural environment of host countries, foreign-owned MNCs can be compared across national boundaries (foreign-owned MNC workers from three different countries are compared in this thesis -- Canada, Brazil, and West Germany). Finally, by grounding the workers' attitudes within social identity theory, divergent attitudes between the workers from the different countries are not only explained, but expected as well.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".