On increasing equity and inclusion of early-career professionals for conferences and conference networking in Canadian fisheries and aquatic science societies
Bibliographic record
Abstract
As early-career professionals (ECPs) navigate their education and professional development in the aquatic sciences, many seek to build a network to help guide their entrance into the field. As influential organizations, scientific societies play a vital role through hosted conferences, where ECPs can meet and share ideas with others, and find mentors to facilitate their colleagues’ journey within the profession. However, not all ECPs are the same, and those from marginalized backgrounds face unique challenges. Here, we provide a perspective on ways scientific societies can ensure all members are provided with equitable opportunity to discover, access, and build career-defining networks at conference events, including the critical role of mentors in navigating obstacles to success. Our recommendations originate from an early-career networking workshop in 2022 at a Canadian fisheries and aquatic sciences conference. The day-long hybrid event comprised interactive activities and discussions on how societies and their conferences can foster and promote inclusive networking for all, including suggestions on maximizing inclusivity for online attendees. This perspective serves as a call to action for scientific societies and senior-career professionals to meaningfully engage with ECPs and marginalized members to promote transformative science.
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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".