Bibliographic record
Abstract
Between species differences in research effort can lead to biases in our global view of evolution, ecology and conservation. The increase in meta-taxonomic comparative analyses on birds underlines the need to better address how research effort is distributed in this class. Methods have been developed to choose which species should be studied to obtain unbiased comparative data sets, but a precise and global knowledge of research effort is required to be able to properly apply them. We address this issue by providing a data set of research effort (number of papers from 1978 to 2008 in the Zoological Record database) estimates for the 10,064 species of birds. We then test whether research effort is associated with phylogeny, geography and eleven different life history and ecological traits. We show that phylogeny accounts for a large proportion of the variance, while geographic range and all the tested traits are also significant contributors to research effort variance. We identify avian taxa that are under- and overstudied and address the importance of research effort biases in evaluating vulnerability to extinction, with non-threatened species studied twice as much as threatened ones. Our research effort data set covering the entire class Aves provides a tool for researchers to incorporate this potential confounding variable in comparative analyses.
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 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".