Introduction to the Special Issue of Qualitative Research in Accounting
Bibliographic record
Abstract
Introduction to the Special Issue of Qualitative Research in AccountingWe are delighted to have been able to act as guest editors for this special issue of Accounting Perspectives on qualitative research in accounting.We are not only grateful to everyone who contributed but also proud of the motivations and outcomes.Conversations around this theme began between Leslie Berger, the former editor-in-chief of the journal, and Philippe Lassou in early 2019, while the latter was organizing the Qualitative Research in Accounting Symposium, which takes place at the University of Guelph every November.Matthew Bamber was invited into the process a few weeks later.Little did any of us know at the time that the world was on the verge of major change.The COVID-19 pandemic made certain that our small talk concerning the weather (after all, it is Canada) and the new term would give way to personal and professional anxiety about the health and well-being of our friends, family, and colleagues.Looking back, who would have known that, mere days later, our calendars would be emptied to make space for online courses about Zoom-based teaching and learning strategies and best practices?One thing, however, did not change during this period-namely, Leslie's energy and commitment to signaling that Accounting Perspectives is a welcoming journal that is open to research regardless of methodological paradigm.A "pluralistic view of research," as it has been termed by Sean Lyons, Associate Dean of Research at the University of Guelph (and an unwavering supporter of the Symposium), is crucial in providing a comprehensive understanding of the interplay between accounting, the economy, and society.This is what Accounting Perspectives promotes.Furthermore, Adam Presslee, the current editor-in-chief, shares this same vision.In many ways, this is the point that we want to make in this editorial-namely, that if you are doing qualitative research in accounting, then you should consider Accounting Perspectives as a potential outlet for your work.The editorial board includes talented and open-minded people from an array of backgrounds who have extensive experience in a range of approaches, many of whom are passionate about high-quality qualitative research in accounting.This special issue features five articles that examine accounting and accountability issues across private for-profit, government, and not-for-profit organizations.All five studies employ case study methods, whether that be a single case (Maharaj; Campbell, Li, Zhang, and Sinclair; Smirnow and Deng), a case in extremis (Popoola and Maier), or multiple cases (Arroyo Pardo, Smaili, and Bensid).Maharaj's study explores the investigation by the US Senate Permanent Subcommittee on Investigations into HSBC operations with respect to alleged money laundering.He shows how the bank uses inscriptions as part of its form-based practices to enact various measures intended to address the requirements of money laundering regulations.In so
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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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.086 | 0.021 |
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".