Economic Inequality Matters: Reflections on Piketty’s Capital in the 21st Century
Bibliographic record
Abstract
In Capital in the 21st Century, Piketty takes a central liberal claim about economic inequality seriously and asks: does capitalism reward merit? If true, we would expect salaries, presumably rooted in the reward of merit in the workplace, to be more important to personal wealth than inherited money and property, which is just luck. He concludes that capitalism does not reward merit more than inherited wealth. Piketty suggests that this is at once a political and moral problem. As such, it cannot be resolved through economics alone, especially in the profession’s current incarnation, characterized by mathematical fetishization. Instead, all of the social sciences and humanities will necessarily be mobilized to develop a full description and analysis of economic inequalities, which must then be made a central question for broad, public debate. This is an important epistemological and political argument, although Capital in the 21st Century has critical weaknesses, including an undertheorized empiricism, a tendency to treat economic inequality as a matter of money and not as a social relationship, and a failure to grasp how class, gender, race and age come together in social relationships of exploitation (and not merely statistical relationship of inequality).
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".