Unpacking the Fluidity of Management Accounting Concepts: An Ethnographic Social Site Analysis of Enterprise Risk Management
Bibliographic record
Abstract
This study offers new insights into what renders management accounting concepts (MACs) fluid. Extant literature depicts how fluidity is an effect of heterogeneous associations among actors, which translate and mobilize them in situated and variegated forms. This focus on heterogeneous arrangements, however, tends to neglect the role of practices and how these practices render MACs fluid. Hence, the study investigates how practices, together with arrangements, which Schatzki (2002) refers to as “site” (i.e., a mesh-work of practice-arrangement bundles), are implicated in MACs’ fluidity. To do so, an ethnography of the MAC “Enterprise Risk Management” (ERM) at the largest division of a multi-national manufacturer was conducted. By analyzing attended risk meetings, the paper shows the ways in which the ERM site prefigures multitudinous paths for carrying on and carrying out risk management activities, which in turn, render the ERM site into a fluid space of intelligibility. These findings indicate that MACs’ fluidity is associated with multidimensional prefigurements that the site produces. With these insights, the paper contributes to understanding how the situated functionality of management accounting comes about, and reveals nuances and multiplicities amid the enabling and constraining space for actions that practiced MACs as mesh-work engender.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".