Bibliographic record
Abstract
This paper attempts to replicate the findings of the recent work, "The rise and fall of biodiversity in literature," by Langer et al. (2021). Using a large corpus from Project Gutenberg (N = ~15,000) and a dictionary-matching method of over 240K biological taxa, Langer et al. find that the frequency and diversity of biological taxa have been declining steadily since the first half of the nineteenth century, echoing prior work in cultural analytics. This paper applies the original paper's three primary measures to two additional data sets along with the original dataset and compares their dictionary-based method with an alternative supervised machine learning method. I find that the trajectory of biological tokens in fiction in the new data sets is directionally opposite to that shown by Langer et al. independent of the methods used (i.e. taxa rise rather than fall since the first half of the nineteenth century) but that their breakpoint estimation appears largely robust within +/- 15 years. Based on this analysis, I suggest that the discrepancy between our results is due to corpus construction rather than choice of method. I find that only conditioning on fiction in the original dataset generates results more similar to the two alternative datasets used here. In addition to emphasizing the importance of corpus construction for cultural analytics, these findings also raise larger questions about the difficulties of interpreting lexical items as indeces of social attitudes, pointing to a need for future work.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".