Editorial: Power, discrimination, and privilege in individuals and institutions
Bibliographic record
Abstract
Editorial on the Research Topic Power, discrimination, and privilege in individuals and institutions "The system is not much concerned if any individual swaps places between levels.The system is concerned that the edifice itself remains intact."-MarieLaurencin Problems operating in oppressive systems include racism, casteism, colorism, sexism, heterocentrism, ethnocentrism, and their intersections.These biases cause issues such as rejection of stigmatized groups, structural racism, disenfranchisement of women, barriers to higher education, economic oppression, radicalization, and colonialism.In this Research Topic, we take a closer look to find the core of the problem, which is inevitably an imbalance in the distribution of power and its misuse.This Research Topic contains 20 articles that cover a range of critical issues in Psychology (Personality and Social, Forensic and Legal, Cultural, and Gender, Sex and Sexualities) and Sociology (Race and Ethnicity, and Gender, Sex and Sexualities).These articles originate with researchers from countries including Germany, China, Singapore, Romania, the USA, and Canada.The researchers submitting these articles identify with a range of ethnicities, including Roma, Indigenous Australian, African American, Mexican American, Southeast Asian, Jewish Canadian, and Black German to name a few. Systems of injusticeSystems of injustice can be found anywhere power is concentrated, including board rooms, editorial offices, university admissions policies, legislative bodies, and organizational bylaws.Policies and procedures may seem fair and appropriate on their face, Frontiers in Psychology frontiersin.org
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".