‘We Can Help!’ Stories of Professional Accountants’ Quest for Social Worth
Bibliographic record
Abstract
The commercialization of the accounting profession has been widely criticized, raising questions about its commitment to serving the public interest. This study nuances these critiques by focusing on professional accountants for whom altruism is a core value. Specifically, we examine the process by which individual accountants attempt to embody altruism by volunteering in accounting humanitarian missions. We theorize accountants’ desire to incarnate altruism as a significance quest in which they attempt to boost their social worth. Through a case study of two humanitarian missions, we uncover how volunteer accountants approached the missions with educational aspirations, wishing to embody altruism by sharing their knowledge. They faced challenges that shaped the situated possibilities of their missions and clashed with their aspirations. To navigate the resulting tensions, they mobilized different means that revealed the difficulties of embodying altruism and the fragility with which it can be accomplished. Our study highlights individual manifestations of altruism and offers a nuanced view of professionals’ understanding of altruism. It also broadens the horizons of how accountants can act in the public interest and integrate social considerations into their practice. Finally, it provides conceptual tools to further understand how accountants can contribute to (or detract from) social issues.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".