Advances in Global Leadership
Bibliographic record
Abstract
We bid a very regretful farewell to Betina Szkudlarek, who partnered with us on Volumes 12-14.Betina has decided to devote as much time as possible to her work with refugees, which goes beyond research to collaborations with many organizations working to improve conditions for refugees.Since she has taken on a global leadership role in that area, we can only wish her well.We will, however, miss Betina's dedication and unerring editorial eye, as well as her unceasing cheerfulness and humor.We are delighted to welcome Martha Maznevski on board, and we're planning to take full advantage of her many talents to put her stamp on AGL.Edited research volumes are often a labor of love, which is certainly true for this book.While a team of four has formally edited this volume, an army of reviewers, students, family members, and friends facilitated and inspired the process, for which we are extremely grateful.This year our special dedications are as follows.Joyce: To the people of Ukraine and their leader, Volodymyr Zelensky, whose circumstances underscore the crucial need for responsible global leadership.Sebastian: To my two daughters Marie and Louisa who are kindly pushing me to constantly reflect on my
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.023 |
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".