Bibliographic record
Abstract
Purpose – The purpose of this paper is to seek to extend the work of Robson (1991, 1992) by exploring the accounting implications of the way in which subsets of non-financial accounting numbers are constructed. In particular, the study investigates whether the different procedures for organising subsets of a set of accounting data may lead to different conclusions about (the same) reality. Design/methodology/approach – The empirical analysis focuses on the procedures by which organisations translate work-related injury outcomes to accounting numbers. First, existing procedures are problematised within their institutional context. This highlights complementary elements of translation and neo-institutional theory that together explain how institutional factors might operate to constrain the problematising process. An empirical analysis of workers’ compensation data covering a ten year period is then conducted to calculate and contrast performance using two competing logics of accounting for injury. Findings – The findings demonstrate that different representations of reality may result not only from accounting choices as to “what” is measured, but also from accounting choices as to “how subsets of measured data are organised”. Specifically, different ways of organising injury data into subsets led to different representations of the reality of overall injury performance. The evidence further suggests taken-for-granted assumptions and institutionalised practices may prevent adequate problematisation of the underpinning logic that guides the procedures for organising translations of work-related injury and illness to accounting numbers. Practical implications – The results suggest the existing logic of accounting for injury fails to recognise the financial or non-financial complexity of non-fatal injury outcomes. “Lost time injury” measures are revealed as neither valid nor reliable measures of injury (or safety) and therefore inappropriate for informing the occupational health and safety (OHS) decisions of managers, boards and external stakeholders. These findings reveal an urgent need for change in injury accounting practice and, in turn, raise serious concerns about the increasingly institutionalised global template for external disclosure of OHS performance information. Originality/value – This paper takes a novel look at the construction of social performance measures and suggests further attention to the construction of accounting subsets is warranted. In demonstrating serious problems in accounting logic that underpin existing, and deeply institutionalised, measurement and reporting practices, the findings reinforce the need to routinely re-problematise accounting practices. Failure to critically review those accounting translations that underpin decision-making may prove a fatal mistake.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".