Editorial: Women in human-wildlife dynamics: 2021
Bibliographic record
Abstract
Fewer than a third of the world's researchers identify as women (Marescotti et al., 2022).Historical and on-going biases, gender stereotypes and other barriers discourage women from entering science-related fields.Barriers come in all shapes and sizes and may often be unintended.Recent research by Huang et al. (2020) has revealed an increasing gender-based gap in publications, associated with high rates of career drop-outs among women.Eagly (2020) expands on this to consider the unequal impact of parenthood, the higher proportion of women in teaching roles within academia (where teaching productivity is typically inversely related to research productivity), and the issue of disproportionate access to (or bias against) internal and external funding, laboratory space, and other resources faced by women researchers.Outside of research, women in conservation face equivalent challenges to career progression and equality in this profession (Jones and Solomon, 2019).Focusing on the conservation field of humanwildlife dynamics (HWD), this special issue provided a platform to better understand the roles and challenges for women in HWD as:• community members and/or leaders living with wildlife; • practitioners and/or researchers working with others who live with wildlife; • advocates, educators, artists and/or innovators for people and wildlife.We invited formats such as storytelling narratives, and biographies which do not easily conform to scientific publishing.However, this facilitated more personal and professional insights into authors' experiences within the field of human-wildlife dynamics, which are largely invisible in empirical research.Guidance on reviewing atypical article types is rare within the natural science literature (but see Byrne, 2016) and we are extremely grateful to our reviewers in this process.Here are the highlights of the 'Women in Human-Wildlife Dynamics" series of article collection Frontiers in Conservation Science frontiersin.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.045 | 0.028 |
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".