International Framework Agreements and the Future of Collective Bargaining in Multinational Companies
Bibliographic record
Abstract
This article emphasizes the contribution of International Framework Agreements (IFAs) to collective bargaining within multinational companies (MNC). For this purpose, we used various data, including content analysis of 42 IFAs and interviews with representatives of the five Global Union Federations (GUFs) involved in the negotiation of these IFAs, in order to assess the content and efficiency of these agreements. Our analysis reveals that IFAs usually include a commitment by the signatory MNC to conform to ILO’s core conventions in all its operations, and to inform its business partners of their obligations under the agreement. Regular meetings are held with unions’ representatives for the monitoring of thes e agreements to ensure their respect by the signatory multinational companies and their business partners. According to GUFs’ representatives, the IFAs have been effective in protecting basic trade union rights covered by ILO conventions 87 and 98. In the last part, we emphasize the IFAs’ contribution to collective bargaining within MNCs and discuss the prospects for future development of international collective bargaining, particularly in light of Levinson’s seminal work (1972) on this subject.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".