Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The abundance of new innovative data sources creates opportunities and challenges for all professions and professionals working with information. One of these professionals is the management accountant (MA). Although their tasks have expanded over time and especially recently, MAs have not fully employed all the available internal and external data sources to describe, diagnose, visualize, predict and prescribe possible solutions that enable smart decisions with positive effects on businesses. Thus, the paper investigates the impact of Big Data, including Data Analytics, on MA’s job profile. Through a review of the most recent academic and professional publications, the paper contributes to the debate surrounding the redefinition of the role of MAs in organizations in a novel informational perspective of Abbott’s theory. The results could serve as a research agenda and incentive for further studies, as well as provide MAs with a guide on the topic of the enlargement of their role(s), respectively, the augmentation of their tasks and responsibilities regarding the analysis of Big Data. Furthermore, the research may provide both a rich and flexible framework to help practitioners in their analysis of potential risks, opportunities and challenges when handling Big Data, and a lens for professional accounting associations and bodies by helping them to prioritize the holding and seizing of jurisdictions as an imperative part of safety and security.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it