Exploring the Roles of Vernacular Accounting Systems in the Development of “Enabling” Global Accounting and Control Systems
Bibliographic record
Abstract
ABSTRACT This study sheds light on how self‐developed local accounting and control systems (so‐called vernacular accounting systems;VAS) can influence knowledge integration in development processes of enabling global accounting and control systems. We focus on accounting and control systems as devices that enable local actors to build on codified knowledge to create “new” knowledge that can facilitate local problem solving. We argue that local actors would evaluate a proposed global system as enabling or coercive depending on both their ability to manipulate the knowledge codified within as well as the consequences that the codified knowledge has for their authority in the local knowledge creation process. Based on a case study of the development process of a global accounting and control system, we demonstrate thatVAScan play a crucial role in both local actors’ evaluation of a proposed system (as points of reference) and their influence on knowledge integration (as knowledge transformation devices). Furthermore, local actors may continue to rely on theirVASif they realize that the proposed global system does not fit their needs. Local actors can thus use theirVAS(as negotiation devices) to strengthen their position in the development process as counterparts to the global system designers because the “threat” of continuing to use theVASmay prompt system designers to integrate local knowledge into the proposed global system. We thus also suggest that, if made visible to others and actively mobilized,VASmay foster productive debates that facilitate the migration of local knowledge into global systems.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".