On Mariana Mazzucato’s Mission Economy: a Moonshot Guide to Changing Capitalism, London, Allen Lane, 2021
Bibliographic record
Abstract
Correspondence: [email protected] Mariana Mazzucato builds a credible case for an expanded role of government and regulation in the purposeful shaping of technological innovation and backs up her case with cogent examples. She joins this to an emerging awareness of the problems that were built into the major innovations of the third industrial revolution by the market. Those innovations, including the basic design of the Internet, we now know, have skill-biases, social biases and use and design biases. These biases were engineered into them not as optimal or efficient responses to social and market forces but as reflections of the cognitive biases of the inventor-engineer class and their financiers. This critique echoes that of even some insiders, such as Jaron Lanier (2011), who argues that the fundamental format of the computing-internet revolution is a ‘roads taken, and not taken’ story of the early days of this revolution. Subsequently, there were non-optimal forms of lock in and path dependency and sedimented power relations. The ideology of ‘disrupt it’ that comes from Silicon Valley has been shown to be a self-serving ideology that is a dangerous mix of libertarian hostility to government combined with a reverence for engineering over social compromise and for unlimited technological rents to the innovators.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".