Removing systemic barriers to equity, diversity, and inclusion: Report of the 2019 Plant Science Research Network workshop “Inclusivity in the Plant Sciences”
Bibliographic record
Abstract
A future in which scientific discoveries are valued and trusted by the general public cannot be achieved without greater inclusion and participation of diverse communities. To envision a path towards this future, in January 2019 a diverse group of researchers, educators, students, and administrators gathered to hear and share personal perspectives on equity, diversity, and inclusion (EDI) in the plant sciences. From these broad perspectives, the group developed strategies and identified tactics to facilitate and support EDI within and beyond the plant science community. The workshop leveraged scenario planning and the richness of its participants to develop recommendations aimed at promoting systemic change at the institutional level through the actions of scientific societies, universities, and individuals and through new funding models to support research and training. While these initiatives were formulated specifically for the plant science community, they can also serve as a model to advance EDI in other disciplines. The proposed actions are thematically broad, integrating into discovery, applied and translational science, requiring and embracing multidisciplinarity, and giving voice to previously unheard perspectives. We offer a vision of barrier-free access to participation in science, and a plant science community that reflects the diversity of our rapidly changing nation, and supports and invests in the training and well-being of all its members. The relevance and robustness of our recommendations has been tested by dramatic and global events since the workshop. The time to act upon them is now.
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 | 0.049 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".