From Foe to Friend
Bibliographic record
Abstract
The relationship between multinational corporations (MNCs) and nongovernmental organizations (NGOs) on social and environmental issues sometimes evolves from being antagonistic to cooperative. To explore how MNCs and NGOs are able to cooperate as friends rather than remain foes, this conceptual research drawing on complexity theory examines a proposed process of mutual adaptation occurring through more flexible semi-structures that support the evolution of (a) joint strategic responses enabled by future gazing, (b) communication systems that facilitate joint strategic responses, and (c) coordinated, timed-based change that supports joint strategic responses. The article provides illustrations from MNC–NGO collaborations. Conclusions are that mutual adaptation and cooperative resolutions are more likely when organizations either share these capabilities or compensate for each other’s shortcomings, and make trade-offs that align with joint strategic objectives. This article contributes to complexity theory and the NGO–MNC literature by exploring how interorganizational cooperative behavior incorporates mutual adaptation so that more sustainable practices are implemented and continuously improved upon by MNCs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".