The Rise of Disease Ecology and Its Implications for Parasitology— A Review
Bibliographic record
Abstract
: Many fields in the biological sciences have witnessed a shift away from organism- or taxon-focused research and teaching in favor of more conceptual and process-driven paradigms. The field of parasitology is no exception, despite the diversity of topics and taxa it encompasses. Concurrently, however, interest in disease ecology has increased dramatically, suggesting new opportunities that merit exploration, as well as the need for parasitology to promote its long history of ecological research to do so. Here we undertake a quantitative analysis of metrics relating to publications, research funding, career opportunities, and undergraduate teaching to comprehensively illustrate the rising prominence of disease ecology. While we distinguish generally between the fields of parasitology and disease ecology, we also emphasize the common interests and complementary approaches that enhanced integration could offer. To illustrate why enhanced integration between these 2 fields is increasingly critical, we highlight 2 successful areas in which parasitology and disease ecology have intersected (community assembly and scale, and the effects of natural enemies on life history traits). We conclude by identifying "frontier topics" that will benefit from greater cooperation and interaction between these currently relatively separate areas and the need for principal investigators to identify and communicate changes in their discipline to students and trainees, which will collectively result in many possible new benefits and prospects for current and future researchers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".