SMPs’ Strategic Responses to Conflicting Normative, Material and Client Constraints
Bibliographic record
Abstract
Abstract The objective of this study is to understand how small and medium audit practices (SMPs) deal with the obligation to conform to evolving norms, given their limited resources and the fact that they serve a client base of mainly small and medium‐sized enterprises (SMEs). Semi‐structured interviews held with 36 respondents working in 33 SMPs revealed that the main response of some SMPs’ to conflicting normative, material and client constraints was to eliminate the normative constraints by exiting the field to which the constraints pertained. When this was impossible, they resorted to less radical strategies. All strategies eventually led to adverse effects, including loss of expertise by some firms and practitioners, a condition that exacerbates the fragmentation within the accounting profession. Disenchanted by an increasingly demanding normative environment, the majority of respondents dealt with their disillusionment by mechanically applying the updates, a process that can lead to lower audit quality. Although the accounting profession was attempting to overcome a confidence crisis by issuing the updates, it ended up ignoring its less visible members and thus triggered upheavals with potentially irreversible consequences. In addition, banks may have been involved in narrowing the audit market for SMPs.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".