Privilege, with Shamus Khan
Bibliographic record
Abstract
What does privilege look like today?How do the advantaged perform "ease"?And why do some of us feel at home in elite spaces, while others feel awkward?Princeton sociologist Shamus Khan joins Uncommon Sense to reflect on elites, entitlement and more.Reminding us that "poor people are not why there's inequality; rich people are why there's inequality", he highlights the importance of studying elites for studying inequality, as the gap between the two grows.Being the author of Privilege: The Making of an Adolescent Elite at St Paul's School (2011), Shamus tells Rosie and Alexis about how the way in which elites justify and see their position has shifted -and how a disability studies perspective helps us to cast a critical eye on the "ease" with which the few seem to nimbly navigate elite institutions.What seems like some of us "have it" and others "just don't" is, suggests Shamus, socially produced -and what appears to be a "flat" and open world, ripe for the bold to seize, is really far more complex.Plus: why might two people who share the same knowledge be valued differently when that knowledge is held in different -racialised and minoritised -bodies?Also: why TV shows and movies about elites don't stop at Saltburn, Succession and The Kardashians?With discussion of Pierre Bourdieu,
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".