Bibliographic record
Abstract
A client fires his financial adviser because his returns lag the market and the returns of his best friend. An adviser constructs portfolios from low-cost index funds, and his clients question why he trades so infrequently—only once a quarter when he rebalances portfolios. An organizer of a conference for financial advisers asks a speaker to refer to the advisers in attendance as “wealth managers,” not “financial advisers.” An adviser argues that robo-advisers can never replace human advisers. These are four of the markers of the financial advising landscape; they represent four challenges financial advisers face. The first marker indicates that some clients see beating the market as the primary service of financial advisers. The second indicates that some clients wonder what advisers do for the fees they charge and sometimes question the fairness of these fees. The third indicates that some advisers are insecure about their roles and the titles that commonly describe them. And the fourth indicates that advisers are aware of the challenges posed by robo-advisers, even as they try to dismiss them. Advisers can meet these challenges by becoming well-being advisers, a role that is rooted in the second generation of behavioral finance, distinct from both standard finance and the first generation of behavioral finance. Standard finance says that investors’ wants are “rational” wants, restricted to the utilitarian benefits of high expected returns and low risk. The first generation of behavioral finance largely accepted standard finance’s notion that investors’ wants are rational, but described actual investors as irrational and offered methods for correcting cognitive and emotional errors. The second generation of behavioral finance (Statman, Finance for Normal People, 2017) describes investors, and people more generally, as normal, distinguishing normal wants from cognitive and emotional errors, and providing guidance on avoiding errors on the way to satisfying wants. The second generation of behavioral finance guides advisers to become well-being advisers. Well-being advisers identify clients’ wants, and help clients assess those wants, balance them, and avoid cognitive and emotional errors on the way to satisfying them.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.144 | 0.039 |
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".