Leadership Luminaries, Cross-cultural empirical analyses of leadership styles and practices
Bibliographic record
Abstract
‘Leadership Luminaries’ provides an invaluable reference point to understand how cultural differences impact upon leadership styles and practices. This new issue of our ongoing global leadership series presents country-specific analyses of culturally endorsed leadership practices and styles in the countries: Australia, Brazil, Canada, Curaçao, Dominican Republic, Egypt, Emirates, Germany, Gibraltar, Great Britain, India, Nepal, Portugal, Romania and Ukraine. This publication contains contributions from around 140 researchers from 38 countries who participated in the Cross-Cultural & Global Business Skills electives offered by the Part-time Academy of the Faculty of Business and Economics at the Amsterdam University of Applied Sciences. The following people contributed: Abel Fego, Adam Prittie, Alaa Jabaly, Олексій Ставіцький (Oleksii Stavitskyi), Alide-Marie Hovenkamp, Amber Bolte, Amber van Nieuwenhoven, Amir Ait Aicha, Amir Kila, Anass Banani, Anastasia Otabil, Anita Elzinga, Anna Csillag, Anna Spinola, Annabel Kruis, Ansa Mohammad, Артем Любенко (Artem Liubenko), 計良歩夢 (Ayumu Keira), Beaudine Overtoom, Ben Oort, Bianca Motta, Carmen Martínez-Almeida García, Caroline Sweep, Casper de Groot, Casper Dokter, Charlotte Dijkman, Chislaine Andrade Costa, Christopher Chin, Cis den Blanken, Clara Weißenhorn, Daan Groot, Daan Tönjes, Dániel Péter Kádár, Daphne Jansen, Diam Mohammed, Dilara Sepetci, Domenico Testa, Dóra Plébán, Douae Merzouki, Douha Moudou, Dounia Belkas, Douwe Schmitt, Dylan Peeters, Dzenis Kuburović, Екатерина Радева (Ekaterina Radeva), Emma Dijkstra, Ericardo Romeo, Erik Oomen, Erin Hoek, Fleur Huurman, Francisca da Conceição Bôto, Frank Mooijer, Gabriela Castillo De Sales, Gaelle Kenjoian, Georgina Addai, Ghizlane Azzaoui, Gianna van Ommeren , Gijs Dekker, Gina Coronel, Hajar El Yakoubi, Hamza Momand, Hanzalah Latif, 김희준 (Heejun Kim), Isabeau Boender, Isak Douah, Ismail Wafelgha, Jamiro Rozendaal, Janou Dihal, Jari Stumeijer, Jeffrey Dominique de Dood, Jessie Peters, Jiya Anwar, Job Pesch, Joe Gimpel, John van der Bent, Juri Siewert, Kaio Leering, Karen Loth, Katherine Landry, Kemeal Khaddage, Kıvılcım Kafkas, Laila Kool, Lara da Fonseca, Latifa El Aissati, Levente Hargitai, Lisa-Marie Cardoso, Maartje Nauta, Manisha Chand, Mantej Dhaliwal, Margaret Maclellan, Mariana Fernandes Cabral, Marit de Zeeuw, Mark van Heijningen, Marlon Clijd, Martijn Carels, Mauro Knebel, Max Bijenveld, Melisa Demiryürek, Mette Kabo, Mike Smith, Milou van Hengel, Mitchell Mugie, Naomi van der Jagt, Nikki van Pelt, Noa Serra de Kloet, Obed Bonsu-Osei, Omayma Amallou Garnat, Pariya Afshintabar, Paul van den Ende, Pelle Brinkhof, Rafi Al Gareb, Ramy Girgis, Renata Calvelli Fonseca, Renske Hogeboom, Roksana Beyer, Sadaf Hamid, Saram Saddiqui, Savino Every, Scarly Mayi Santos, Selman Muğlu, Sergio Mendez Vilas, Shanna Strube, Sofia Kontaktsiou, Soraya Panoet, Tamara Liefting, Thea Hughes, Tom Remmerswaal, Vanessa Vieira de Sousa, Vlad Milosteanu, Waiz Malik, Warsha Tamang, William Horsford, Zach Saine, Ziad Elwakeel and Zineddine Rhninou.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".