Remembering the scholarship of Nathan Sears: A forum <i>in memoriam</i>
Bibliographic record
Abstract
The team at Global Policy: Next Generation were heartbroken to hear of the tragic and untimely passing of Dr. Nathan Alexander Sears earlier this year. GPNG is an initiative focused on amplifying the scholarship of early career researchers—exceptional thinkers at the beginning of their professional academic careers. As editors and as colleagues, we were not prepared to be writing in memoriam about one of our earliest contributors. Dr. Sears contributed an exceptional article to the first edition of Global Policy: Next Generation (2020). It remains GPNG's most highly cited and engaged-with publication. In the video abstract of the article found here, Dr. Sears discusses the paper's argument and contribution to the field. Among the many tragedies of Dr. Sears' passing is that his scholarly career was cut short just as it was beginning. He had defended his PhD dissertation at the University of Toronto in October 2022. The recency of this milestone achievement, however, belies a career already full of accomplishments, including work as a Government of Canada Cadieux-Léger policy fellow and several influential articles (Sears, 2017, 2020, 2021; Smith et al., 2020). Dr. Sears' work was at the cutting edge of research on global existential risk, using the tools of International Relations theory to understand why great powers choose to cooperate—or not—on human-induced civilisational threats from nuclear war to bioengineered pathogens to ‘unaligned’ artificial intelligence. Dr. Sears' death is a profound loss for both the research and policy communities with which he so passionately engaged. His passing also leaves a hole in our academic community that is wider than just his research. Nathan was an engaging and passionate scholar who positively contributed to every community he was a part of. He was a thoughtful and respectful listener and a generous and enthusiastic peer. Despite the seriousness of his work and his intense concern with existential risk, Nathan was also known as someone with a great sense of humour—quick to laugh and armed with a bright and infectious smile. This forum is a tribute to Dr. Sears' life through a reflection on his scholarship and research career. The five contributions consider the impact of Dr. Sears' published and unpublished research and the ideas he developed over the course of his too-brief career. The contributors pay their respects to Dr. Sears in the best way scholars know how—by engaging with, debating with, critiquing and expanding on his ideas. They also speculate about where Nathan's work may have gone in the future and the contributions to humanity he may have made. Those who knew Dr. Sears know that he relished intellectual debate: he would have truly enjoyed reading these reflections. Our hearts go out to Nathan's family and all those in our community who are mourning him and the loss of such enormous potential. We hope this forum can amplify Dr. Sears' work so that his scholarship and intellectual contributions can live on. Emma Lecavalier, University of Toronto, Board Member and former Deputy Editor, Global Policy: Next Generation. Gregory Stiles, University of Sheffield, Editor, Global Policy: Next Generation. Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.030 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".