Bibliographic record
Abstract
Inequality remains prevalent in society. Just from the perspective of monetary income and wealth, women earn only 79 cents for every dollar that men earn (Council of Economic Advisers, 2016), blacks households have on average only one twentieth of the wealth of white households (Taylor, Kochhar, Fry, Velasco, & Motel, 2011), and the top decile of American households own over seventy percent of the total net wealth in the country (Piketty & Saez, 2014). In this symposium, we showcase research that provides insight into how we may reduce some of this inequality. The first paper investigates gender differences in sponsorship and mentorship behaviors, suggesting that increased sponsorship may be a way to promote and retain women within organizations. The second paper explores when and why people acknowledge privilege, providing a framework to understand who is likely to rectify systemic inequality and insights into how to encourage others to acknowledge and address privilege. The third paper investigates the effectiveness of a short, one-time online intervention that attempts to create long-term behavioral change with regards to reducing gender bias and stereotyping in the workplace. Finally, the fourth paper examines settings in which working-class individuals may perform better than their middle-class counterparts, suggesting ways to harness interdependence to reduce class-based inequality. Following the presentations, Aparna Joshi, a major contributor in the fields of workplace diversity, gender, and inequality in organizations, will serve as our symposium's discussant. Together, these four papers and a discussion led by Professor Joshi will advance our understanding of contributing factors to inequality within organizations and what steps we can take to begin reducing inequality in organizations.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 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".