Shades of Cultural Marginalization: Cultural Survival and Autonomy Processes
Bibliographic record
Abstract
The recent rise in extremism, authoritarianism, displacement and isolationism signals troubled times for the most marginalized groups in societies. In this article, our primary emphasis is on a specific aspect of marginalization within organizational theory – referred to as cultural marginalization. We argue that the existing literature lacks an adequate theoretical understanding to address this phenomenon. To theorize cultural marginalization and uncover how marginalized groups may cope with such circumstances, we build on and problematize the culture-as-toolkit perspective. We integrate this perspective with other cultural theories that consider power structures more prominently. Drawing on this theoretical base, we develop a typology of four dynamics of cultural marginalization and conceptualize the specific cultural survival and cultural autonomy processes marginalized groups may undertake to safeguard their culture. In doing so, we contribute to the ongoing debate surrounding the toolkit perspective by providing novel insights into how marginalized groups utilize their socio-culturally constrained cultural resources in distinct ways, compared with more resourceful actors and groups. Our theoretical contributions pave the way for new avenues of research to deepen our understanding of the general process of cultural marginalization and to direct further inquiry into the survival strategies of marginalized groups and how they might (re)gain autonomy.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".