Bibliographic record
Abstract
Two-thirds of Americans report that they would take two extra weeks of vacation above two extra weeks of salary, and half of all business professionals report that their jobs offer no “meaning or significance.” And after working all day at jobs we hate, we buy things we don’t need. In UTOPIA FOR REALISTS, Dutch historian and journalist Rutger Bregman reminds us it needn’t be this way. A manifesto full of intentionality and pragmatism, Bregman’s book centers on three central utopic ideas: a 15-hour workweek, a “universal basic income”, no strings attached, and open borders throughout the globe. Though the claims might seem fanciful at first, UTOPIA FOR REALISTS provides numerous examples of successful experiments with “free money”, such as Mincome in 1970s Canada, and experiments in giving homeless people a financial foundation. The theory among detractors is that free money will make people be lazy and work less. But in fact, employment is necessary for virtually everyone’s happiness. As for the workweek, global studies show again and again that a shorter workweek contributes to lower stress, lower environmental impact, fewer work mistakes or accidents, lower gender inequality, and lower wealth inequality. Although the ideas here may sound impossible to some, Bregman argues that change begins with an idea–and we must be at the ready when we can no longer sustain our hyperproduction and consumption. In UTOPIA FOR REALISTS, Bregman shows us the most unrealistic economic system is the one we’re already living in.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.072 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".