What are the ‘most influential people in accounting’ saying about the ‘most important issues currently facing the accounting profession’?
Bibliographic record
Abstract
Drawing on theories of influence derived from social psychology, this article studies Accounting Today’s 2023 list of The Top 100 Most Influential People in Accounting (‘the List’). For many reasons the List is controversial, but it is also a window into the profession, providing readers insights into where key players think it is, where it is going, and what it aspires to be. This article analyzes what those on the List consider to be the most important issues currently facing the accounting profession, and what they think the solutions are. A number of core themes emerge as salient, namely: (a) the pipeline problem, (b) the adoption and application of new technologies, (c) the struggle for relevance of (some) accounting work, and (d) the accounting workplace and human capital management. The solutions presented include: (i) accounting needs better branding and marketing, (ii) need to adopt and use new technologies in a range of creative, thoughtful, and compassionate ways, (iii) accounting workplaces and work conditions need to be improved, not least compensation for new entrants. Ultimately, this article’s core thesis is that the key challenges (opportunities) are fundamentally inter-related and inter-connected. Thus, a strategy which involves one group sitting back and hoping that another will fix a stand-alone issue while they watch on is a strategy that seems destined to fail and will cost us dearly. As such, holistic, ‘big tent’, consensus-garnering solutions are required.
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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".