Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it