Reflections From <i>Advances in Global Leadership</i> 's Emerald Literati Award Winners
Bibliographic record
Abstract
The book/journal editors of Emerald Publishing are asked to select the Outstanding Author Contribution in each volume, which is a difficult choice. Before the COVID-19 pandemic, the Emerald Literati Awards were handed out in a ceremony at the Academy of Management Meeting. Because that practice ended, we decided to showcase the work of our award winners, beginning with volume 8, who have made very important contributions to the field of global leadership. We were also very curious about the impact of their article and what they would write differently today. Thus, we invited the author/author team to write a short reflective piece broadly related to the questions below.(1) What motivated you to research this topic?(2) Do you have any sense of what impact your paper has had on the field of global leadership or beyond?(3) Would you write this paper differently in retrospect, or if you were writing it today? Is there anything you would add or change?(4) Did the paper have any impact on you personally? For example, did it change the way you teach, influence what you are researching today, get you promoted and put you in a higher income bracket (just kidding), etc?
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".