Episode 40: Syriana (2005) (Guest: Peggy McGuinness)
Bibliographic record
Abstract
Syriana is a 2005 geopolitical thriller written and directed by Stephen Gaghan, based loosely on former CIA case officer Robert Baer’s memoir, See No Evil: The True Story of a Ground Soldier in the CIA's War on Terrorism. The film weaves together multiple storylines that involve a CIA agent, a U.S. energy analyst, a major transnational law firm, and an oil-rich Persian Gulf kingdom. It tackles complex themes of corruption, power, and terrorism from a distinctly post-9/11 vantage point. The film also suggests how law operates in transnational settings and how it seeks—but often fails—to tame the forces of ambition, greed, and power that drive the oil industry and America’s role in it. Guest: Margaret (“Peggy”) McGuinness Margaret (Peggy) McGuinness joined the St. John’s faculty in 2010. Professor McGuinness researches and teaches in the areas of international law and international human rights law. She has published widely on the subjects of international human rights law, international security and the resolution of armed conflict, and the role and influence of international law in the United States. Professor McGuinness’s current research examines U.S. diplomacy and its influence on international human rights governance. Her recent work includes, Human Rights Reporting as Human Rights Governance, published in Columbia Journal on Transnational Law. She is also the co-editor (with David Stewart, Georgetown Law) of the forthcoming Edward Elgar Research Handbook on Law and Diplomacy (forthcoming 2021). Professor McGuinness serves on the Council on International Affairs of the New York City Bar and the Executive Committee of the International Section of the New York State Bar Association, where she is also co-chair of the Public International Law Committee. Professor McGuinness previously worked as a litigator for Paul, Weiss, Rifkind, Wharton & Garrison LLP. Her career in the law follows an early career as a Foreign Service Officer with the State Department, which included service in Germany, Pakistan and Canada, and as a Special Assistant to Secretary of State Warren Christopher. Timestamps: 0:00 Introduction3:00 The context and setting 5:24 The film’s multiple storylines8:28 Former CIA agent Robert Baer19:22 Capital markets and energy derivatives25:26 Big oil in the early 2000s and today28:28 Big law and the Jeffrey Wright character 33:43 DOJ’s investigation37:14 The Foreign Corrupt Practices Act 42:40 The illusion of due diligence 47:40 Radicalization53:06 Gulf monarchs 55:10 Targeted assassination1:01:14 The next movie: big tech and AI1:01:52 The outcome Further Reading: Alyson, Brusie et al., “Foreign Corrupt Practices Act,” 61 Am. Crim. L. Rev. 713 (2024) Baer, Robert, See No Evil: The True Story of a Ground Soldier in the CIA's War on Terrorism (Crown, 2003) Kahrl, William L., “The Politics of the California Water: Owens Valley and the Los Angeles Aqueduct, 1900 – 1927,” Hastings West-Northwest J. Envt’l L. & Policy, vol. 6, nos. 1 & 2 (2000) Lewis, R. James & Awan, Akil N. eds. Radicalization: A Global and Comparative Perspective (Oxford Univ. Press, 2024) Stiglitz, Jospeh E., Globalization and Its Discontents (W. W. Norton & Co. 2002)
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.107 | 0.018 |
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".