Early career researchers benefit from inclusive, diverse and international collaborations: Changing how academic institutions utilize the seminar series
Bibliographic record
Abstract
Efforts to make research environments more inclusive and diverse are beneficial for the next generation of Great Lakes researchers. The global COVID-19 pandemic introduced circumstances that forced graduate programs and academic institutions to re-evaluate and promptly pivot research traditions, such as weekly seminar series, which are critical training grounds and networking opportunities for early career researchers (ECRs). While several studies have established that academics with funded grants and robust networks were better able to weather the abrupt changes in research and closures of institutions, ECRs did not. In response, both existing and novel partnerships provided a resilient network to support ECRs at an essential stage of their career development. Considering these challenges, we sought to re-frame the seminar series as a virtual collaboration for ECRs. Two interdisciplinary graduate programs, located in different countries (Windsor, Canada, and Detroit, USA) invested in a year-long partnership to deliver a virtual-only seminar series that intentionally promoted: the co-creation of protocols and co-led roles, the amplification of justice, equity, diversity and inclusion throughout all aspects of organization and representation, engagement and amplification through social media, the integration of social, scientific and cultural research disciplines, all of which collectively showcased the capacity of our ECRs to lead, organize and communicate. This approach has great potential for application across different communities to learn through collaboration and sharing, and to empower the next generation to find new ways of working together.
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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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.027 | 0.012 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".