sj-docx-1-spp-10.1177_19485506221129687 – Supplemental material for Gendered Self-Views Across 62 Countries: A Test of Competing Models
Bibliographic record
Abstract
Supplemental material, sj-docx-1-spp-10.1177_19485506221129687 for Gendered Self-Views Across 62 Countries: A Test of Competing Models by Natasza Kosakowska-Berezecka, Jennifer K. Bosson, Paweł Jurek, Tomasz Besta, Michał Olech, Joseph A. Vandello, Michael Bender, Justine Dandy, Vera Hoorens, Inga Jasinskaja-Lahti, Eric Mankowski, Satu Venäläinen, Sami Abuhamdeh, Collins Badu Agyemang, Gülçin Akbaş, Nihan Albayrak-Aydemir, Soline Ammirati, Joel Anderson, Gulnaz Anjum, Amarina Ariyanto, John Jamir Benzon R. Aruta, Mujeeba Ashraf, Aistė Bakaitytė, Maja Becker, Chiara Bertolli, Dashamir Bërxulli, Deborah L. Best, Chongzeng Bi, Katharina Block, Mandy Boehnke, Renata Bongiorno, Janine Bosak, Annalisa Casini, Qingwei Chen, Peilian Chi, Vera Cubela Adoric, Serena Daalmans, Soledad de Lemus, Sandesh Dhakal, Nikolay Dvorianchikov, Sonoko Egami, Edgardo Etchezahar, Carla Sofia Esteves, Laura Froehlich, Efrain Garcia-Sanchez, Alin Gavreliuc, Dana Gavreliuc, Ángel Gomez, Francesca Guizzo, Sylvie Graf, Hedy Greijdanus, Ani Grigoryan, Joanna Grzymała-Moszczyńska, Keltouma Guerch, Marie Gustafsson Sendén, Miriam-Linnea Hale, Hannah Hämer, Mika Hirai, Lam Hoang Duc, Martina Hřebíčková, Paul B. Hutchings, Dorthe Høj Jensen, Serdar Karabati, Kaltrina Kelmendi, Gabriella Kengyel, Narine Khachatryan, Rawan Ghazzawi, Mary Kinahan, Teri A. Kirby, Monika Kovacs, Desiree Kozlowski, Vladislav Krivoshchekov, Kuba Kryś, Clara Kulich, Tai Kurosawa, Nhan Thi Lac An, Javier Labarthe-Carrara, Mary Anne Lauri, Ioana Latu, Abiodun Musbau Lawal, Junyi Li, Jana Lindner, Anna Lindqvist, Angela T. Maitner, Elena Makarova, Ana Makashvili, Shera Malayeri, Sadia Malik, Tiziana Mancini, Claudia Manzi, Silvia Mari, Sarah E. Martiny, Claude-Hélène Mayer, Vladimir Mihić, Jasna MiloševićĐorđević, Eva Moreno-Bella, Silvia Moscatelli, Andrew Bryan Moynihan, Dominique Muller, Erita Narhetali, Félix Neto, Kimberly A. Noels, Boglárka Nyúl, Emma C. O’Connor, Danielle P. Ochoa, Sachiko Ohno, Sulaiman Olanrewaju Adebayo, Randall Osborne, Maria Giuseppina Pacilli, Jorge Palacio, Snigdha Patnaik, Vassilis Pavlopoulos, Pablo Pérez de León, Ivana Piterová, Juliana Barreiros Porto, Angelica Puzio, Joanna Pyrkosz-Pacyna, Erico Rentería Pérez, Emma Renström, Tiphaine Rousseaux, Michelle K. Ryan, Saba Safdar, Mario Sainz, Marco Salvati, Adil Samekin, Simon Schindler, A. Timur Sevincer, Masoumeh Seydi, Debra Shepherd, Sara Sherbaji, Toni Schmader, Cláudia Simão, Rosita Sobhie, Jurand Sobiecki, Lucille De Souza, Emma Sarter, Dijana Sulejmanović, Katie E. Sullivan, Mariko Tatsumi, Lucy Tavitian-Elmadjian, Suparna Jain Thakur, Quang Thi Mong Chi, Beatriz Torre, Ana Torres, Claudio V. Torres, Beril Türkoğlu, Joaquín Ungaretti, Timothy Valshtein, Colette Van Laar, Jolanda van der Noll, Vadym Vasiutynskyi, Christin-Melanie Vauclair, Neharika Vohra, Marta Walentynowicz, Colleen Ward, Anna Włodarczyk, Yaping Yang, Vincent Yzerbyt, Valeska Zanello, Antonella Ludmila Zapata-Calvente, Magdalena Zawisza, Rita Žukauskienė and Magdalena Żadkowska in Social Psychological and Personality Science
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.834 | 0.420 |
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".