Reclaiming the Past to Shape the Future: Examining Diversity, Equality, and Inclusion in South Asia
Bibliographic record
Abstract
Global shifts in the geopolitical, environmental, demographic, and technological landscape are introducing unprecedented levels of uncertainty into labor markets and employment relations. With the world waiting for answers, the onus is on management scholars to offer new theoretical approaches and evidence-based insights that might allow managers, policymakers, and labor leaders to more effectively and collaboratively meet the challenges presented (AOM, 2022). This global shift also demands that organizations and organizational scholars pay greater attention to diversity, equity, and inclusion (DEI) practices. Research on DEI practices has been conducted primarily on mono-cultural Western-oriented or “WEIRD” (i.e., Western, Educated, Industrialized, Rich, & Democratic; Henrich, 2021) countries (Nishi & ?zbilgin, 2007; Rad et al., 2018). However, legislative frameworks, political, societal, religious, and governance factors result in DEI practices that vary from country to country and differ considerably from the West (?zbilgin & Syed, 2010; Klarsfeld et al., 2022). Countries in the South Asian region—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka—are multicultural developing economies that vary widely along various factors, including education, democracy, and degree of industrialization, among others. The South Asian region is also home to three (i.e., India, Pakistan, & Bangladesh) of the world's most- populated countries (Neufeld, 2021; Worldometer, 2023), and has become increasingly critical to the global economy (IMF, 2019). Yet, there is a dearth of research on DEI in South Asia (Saifuddin et al., 2022; Syed & Pio, 2013), which not only restricts our knowledge, but also hampers our field’s ability to provide guidance to organizations, governments, and other stakeholders on how to structure and implement effective DEI policies and programs. Decolonizing the Mind: Reclaiming the Past to Reframe the Future Author: Samina M. Saifuddin; Morgan State U. Striving for Inclusive Organizations–The Importance of Context: Evidence from MNC Subsidiaries Author: Sana Ahmed; Henley Business School, U. of Reading, United Kingdom Contextualizing the Wheel of Privilege in the Case of Nepal Author: Alina Spanuth; U. Autónoma de Barcelona Symbolic Women's Leadership in Bangladesh: Combating the Patriarchal Culture and Mindset Author: Faria Rashid; George Mason U. Author: Samina M. Saifuddin; Morgan State U. Comparing Women’s Workforce Diversity, Equality, and Inclusion in South and East Asia Author: Hyunji Yi; - Embracing Diversity: South Asian Organizational Traditions and Lessons for Global DEI Practices Author: Salma Akther; Louisiana State U. Author: MUHAMMAD RUHUL AMIN; PhD in Management Candidate
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".