Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits
Bibliographic record
Abstract
This research assesses the evolution of lexical diversity in scholarly titles using a new indicator based on zipfian frequency-rank distribution tail fits. At the operational level, while both head and tail fits of zipfian word distributions are more independent of corpus size than other lexical diversity indicators, the latter however neatly outperforms the former in that regard. This benchmark-setting performance of zipfian distribution tails proves extremely handy in distinguishing actual patterns in lexical diversity from the statistical noise generated by other indicators due to corpus size fluctuations. From an empirical perspective, analysis of Web of Science (WoS) article titles from 1975 to 2014 shows that the lexical concentration of scholarly titles in Natural Sciences & Engineering (NSE) and Social Sciences & Humanities (SSH) articles increases by a little less than 8% over the whole period. With the exception of the lexically concentrated Mathematics, Earth & Space, and Physics, NSE article titles all increased in lexical concentration, suggesting a probable convergence of concentration levels in the near future. As regards to SSH disciplines, aggregation effects observed at the disciplinary group level suggests that, behind the stable concentration levels of SSH disciplines, a cross-disciplinary homogenization of the highest word frequency ranks may be at work. Overall, these trends suggest a progressive standardization of title wording in scientific article titles, as article titles get written using an increasingly restricted and cross-disciplinary set of words.
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.
How this classification was reachedexpand
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.065 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".