Capitalism, coronavirus, and war in the digital age: Interview with Radhika Desai
Bibliographic record
Abstract
In Spring 2024, I met Radhika Desai for the first time at the London School of Economics and Political Science when we both held visiting positions at the Department of International Development as invited scholarly visitors. Although I had not expected her presence, I immediately recognized her after having been a reader of her works on geopolitical economy when developing my own ideas about the political economy of Chinese media and communications. I introduced myself and started talking with her, first at a coffee shop, then in a small office inside LSE’s Connaught House, and later at the Marx Memorial Library in London for a book launch party. Prompted by my questions rooted in the field of media and communication, Radhika Desai shared ideas from her newly published book, Capitalism, Coronavirus, and War: A Geopolitical Economy (Routledge, 2023), which is being translated into Chinese, her critiques about imperialism, globalization and essentially the world order after decades of neoliberalism, and, furthermore, her hopes for China, BRICS, and, ultimately, for the political left. Now, these conversations are turned into a dialogue piece, and with it I hope Communication and the Public readers will sense my gains from Radhika Desai, that is, to paraphrase from the famous line from the Communist Manifesto, in the digital age when all that is solid melts into ostensibly immaterial communication, man/woman is at last compelled to face with sober senses the real conditions of life and his or her relations with their kind.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".