Historical time in the age of big data: Cultural psychology, historical change, and the Google Books Ngram Viewer.
Bibliographic record
Abstract
Launched in 2010, the Google Books Ngram Viewer offers a novel means of tracing cultural change over time. This digital tool offers exciting possibilities for cultural psychology by rendering questions about variation across historical time more quantitative. Psychologists have begun to use the viewer to bolster theories about a historical shift in the United States from a more collectivist to individualist form of selfhood and society. I raise 4 methodological cautions about the Ngram Viewer's use among psychologists: (a) the extent to which print culture can be taken to represent culture as a whole, (b) the difference between viewing the past in terms of trends versus events, (c) assumptions about the stability of a word's meaning over time, and (d) inconsistencies in the scales and ranges used to measure change over time. The aim is to foster discussion about the standards of evidence needed for incorporating historical big data into empirical research.
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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 | 0.034 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".