Conflicting Accounts of Inclusiveness in Accounting Firm Recruitment Website Photographs
Bibliographic record
Abstract
This study offers a contribution to diversity and inclusion research and the professional practices of accounting firms by critically interpreting representations of gender and ethnic diversity in recruitment photographs using a critical visual methodology. We analyze photographs from the recruitment websites of public accounting firms for depictions of gender and ethnic inclusiveness using a Barthesian approach. We first analyze and interpret the denotative and connotative content of 1493 photographs. We then connotatively interpret the text and photographs in two particularly salient recruitment documents using critical semiotics. We find women (non-white individuals) make up approximately half (one quarter) of the people depicted, roughly matching trends in the population. However, women and non-white individuals are frequently depicted in subordinate roles. We conclude that while women and non-white individuals are denotatively ‘present’ in recruitment photographs, they are constructed connotatively as ‘other’ in public accounting, consistent with hegemony. We argue women and non-white individuals are generally constructed as outsiders, in spite of their numerical presence in the photographs. Further, we argue firms should be aware of various possible connotative interpretations of their photographs, as these interpretations may conflict with the accounts with respect to diversity and inclusion conveyed in photographs’ denotative content.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".