The World Is Going to Hell, the Young No Longer Respect Their Elders, and Other Tricks of the Mind
Bibliographic record
Abstract
The notion of a “Millennial” generation, much like a “Generation X” or the “Baby Boom” generation, with a strong coherence in terms of values and norms that differ from previous cohorts, has been of dependable interest in the popular press. However, given what we know regarding the proportion of trait expression due to sources largely immune to cohort effects (e.g., large genetic contributions), how difficult it is for us to systematically influence their expression (e.g., small long-term parental effects), and the massive variation within groups, the meta-analytic work of Costanza, Fraser, Badger, Severt, and Gade (2012) underscores what should already be known from first principles; generation or cohorts are inevitably a poor predictor of anything. The literature on ingroup/outgroup bias (Hogg & Abrams, 1990), stereotype formation (Mackie, Hamilton, Susskind, & Rosselli, 1996), and reconstructive memory issues (Schacter, 1999) provides ample underlying evidence for how these generational overgeneralizations form.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".