Technical accounting resources as performative <i>topoi</i> for sustainability
Bibliographic record
Abstract
Purpose The purpose of this study is to explore the potential for technical accounting resources to help professional accountants exercise their performative agency. Design/methodology/approach The authors combine the integrative learning theory of truth and the concept of performativity, including two approaches to sustainability education and interventions, to construct a grid for coding the technical resources provided by the UK's Institute of Chartered Accountants in England and Wales, a pioneer in sustainability advocacy. Findings The findings suggest the dominance of the “predetermined and expert-determined” approach. They also reveal the emergence of three levels of performative topoi based on the relative presence of the “predetermined and expert-determined” and “process-of-seeking” approaches to professional interventions toward sustainability. The results show the profession's evolving contribution to the construction of actionable knowledge. Research limitations/implications The main limitation of this research is that it draws on a limited corpus. In addition, the use of a binary code to represent the presence/absence of a code does not convey the code's quantitative importance. Practical implications The results are useful for those wanting to produce technical accounting resources that are more likely to help professionals build actionable knowledge and contribute to accountants' interventions toward sustainability. Social implications Findings suggest the need for reflection on how the accounting profession can best contribute to implementing sustainability in organizations. Originality/value Few studies deconstruct professional technical resources to see how a profession can contribute to a process of societal change.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".