Differentiating the Corporation: Accountability and International Humanitarian Law
Bibliographic record
Abstract
Corporations are significant global actors that are continuing to gain international legal status. Regulatory efforts have closely followed persistent claims that various forms of corporate activity are adversely affecting individual welfare and societal objectives. Such observations are perhaps most acute during instances of armed conflict. The history of corporate misdeeds occurring within or contributing to the perpetuation of warfare is now well-documented. However, the relationship between international humanitarian law—the legal field governing the conduct of war—and corporations receives less attention than other areas of international law where the treatment of business entities have made important advancements. This article considers the particularities that affect how accountability is imposed for corporate behavior that implicates IHL. Accordingly, the article has three purposes. First, it describes the (indirect) doctrinal methods through which accountability for corporate conduct implicating IHL may be pursued. Second, it identifies structural challenges and features of the corporate form that compromise the efficacy of these methods and result in accountability gaps. Third, through a series of case studies—addressing the conduct of Blackwater in Iraq, Facebook in Myanmar, and Airbnb in the West Bank—the article categorizes disparate forms of corporate conduct that implicate IHL in previously unforeseen ways and present unidentified regulatory challenges. Collectively, the article suggests that if international law is to contribute to the process of narrowing accountability gaps, if it is to provide an agreeable and accurate vocabulary for determining standards and adjudging conduct, regulatory efforts must begin by embracing those features that differentiate the corporation from those other entities that have traditionally held international law’s attention.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".