Bibliographic record
Abstract
Economics has hit the mainstream in the last decade with popular books like Freakonomics and The Undercover Economist reaching the masses. These authors have used their toolkits far beyond the narrow scope of money and finance and answered questions pertaining to anything from social policy to demographics to crime. Their appeal has largely been their ability to explain that small underlying forces can have major impacts, intended or otherwise, on many different areas of society. One recent book following this trend is Nudge, published in 2008 by University of Chicago academics Richard Thaler and Cass Sunstein. The book has attracted acclaim from both journals and the press, with The Financial Times naming it as one of the best business books for 2008. Nudge coins the term ‘choice architecture’, referring to the manner in which a range of alternatives is presented, which the authors contend is commonly overlooked as an integral part of many decisions we all face during the course of our day-to-day lives (1). When people take the time to judiciously research all alternatives before them, or use their reflective systems in the parlance of the book, they generally make objectively good decisions. Unfortunately, in practice people cannot or do not take the time to do so and instead use their automatic or gut thinking systems, leading to inferior outcomes. The first section of the book then compellingly demonstrates the evidence of its importance in a multitude of situations. There are many lessons to be learned along the way, applicable to both policy-makers and those who wish to critically examine some of their own choices in life. Among these, lessons is the fact that a large percentage of the population will stick with an easy default option without consideration of better alternatives, even when considering a life-altering decision such as retirement planning. There are even examples of people who fail to take advantage of subsidies to supplement their retirement income by simply not filling out the necessary application form. A further counter-intuitive warning for policy-makers is that in some cases, giving people too many choices can lead to worse outcomes as a result of being overwhelmed, or unable to accurately assess them all. Given the evidence, policy-makers should take heed that acknowledging choice architecture can prove as essential to a policy’s success as the choices themselves.
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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.032 | 0.047 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".