Evolution of the Moral Lexicon
Bibliographic record
Abstract
Morality is central to social well-being and cognition, and moral lexicon is a key device for human communication of moral concepts and experiences. How was the moral lexicon formed? We explore this open question and hypothesize that words evolved to take on abstract moral meanings from concrete and grounded experiences. We test this hypothesis by analyzing semantic change and formation of over 800 words from the English Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. Across historical text corpora and dictionaries, we discover concrete-to-abstract shifts as words acquire moral meaning, in contrast with the broad observation that words become more concrete over time. Furthermore, we find that compound moral words tend to be derived from a concrete-to-abstract shift from their constituents, and this derivational property is more prominent in moral words compared to alternative compound words when word frequency is controlled for. We suggest that evolution of the moral lexicon depends on systematic metaphorical mappings from concrete domains to the moral domain. Our results provide large-scale evidence for the role of metaphor in shaping the historical development of the English moral lexicon.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".