Addressing the “Social” in Social Class: An Interpersonal Perspective of Class in Organizations
Bibliographic record
Abstract
Social class plays an integral role in how individuals connect with others and make sense of their interactions. To date, organizational literature in class tends to focus on individual-level processes and outcomes such as self-efficacy, income, and job search successes. Previous research in management, social psychology, and sociology, however, highlights the importance of adopting an interpersonal lens to understand social class. In organizations, for example, interactions between employees may draw out and perpetuate social class distinctions or disrupt class-based hierarchies. It is critical to understand better how social class impacts and is impacted by interactions, as workplace interactions underpin organizations and are the main mechanism by which work is accomplished. As such, this symposium explores how social class is manifest in, and subsequently exerts influence on employees’ interpersonal exchanges. Sticky Social Class: A Dynamic Perspective on Subjective Social Class in the Workplace Author: Elizabeth Johnson; Harvard Business School Author: L Taylor Phillips; NYU Stern Author: Julian Jake Zlatev; Harvard Business School Emboldened by Power: Interaction of Social Class and BATNA on Entitlement Author: Shawn Xiaoshi Quan; U. of Washington Author: Elijah Wee; U. of Washington Understanding Gender Identity, Social Class, and Relational Reconciliation at Work Author: Philip DeOrtentiis; Michigan State U. Author: Gia Ruscitto; Michigan State U. Transitioning into the Workplace: A Qualitative Investigation of Upwardly Mobile College Graduates Author: Jennifer J. Kish-Gephart; U. of Massachusetts, Amherst Author: Kristie Joy Neff Moergen; Iowa State U. Author: Jacqueline Tilton; Appalachian State U.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".