Diversity, Equity, and Social Justice in Accounting Education
Bibliographic record
Abstract
ABSTRACT Diversity, equity, and social justice are now ever more salient for organizations as we all grapple with how to create a more just, compassionate, and humane world. The accounting profession, with its traditional norms and practice, is no stranger to decades of discrimination, which has proven challenging to address despite visible outcomes of victimization and marginalization of multiple groups (Hammond 1995; Annisette 2003; Anderson-Gough, Grey, and Robson 2005; Carmona and Ezzamel 2016; Rumens 2016; Rosenthal 2019; Brown-Liburd and Joe 2020). Marginalized groups include, for example, people from historically oppressed racial, religious, ethnic, and cultural backgrounds; people of nonconforming gender or noncisgender and of nonheterosexual orientations; people with disabilities; and people from low socioeconomic backgrounds and their intersections.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".