LEADS US NOT INTO TEMPTATION : KNOWLEDGE WORKERS , BUSINESS INTELLIGENCE SYSTEMS , AND OCCUPATIONAL FRAUD
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
This paper explores how uncertainty reduction due to increased forecasting accuracy, which is one of main benefits associated with the adoption of Business Intelligence (BI) systems, will affect the behavior of knowledge workers and how this change in their behavior will impact the appropriation of benefits from BI investments. The study uses a micro-economic model in order to show that higher forecasting accuracy is likely to create the conditions for knowledge workers to behave in a morally hazardous fashion. The result of this opportunistic behavior is that knowledge workers can appropriate for themselves a relatively larger portion of the firm’s rents from BI investments that should accrue to firm and ultimately to external stakeholders. Studies that measure the payoffs from IT investments that enable more accurate forecasts, such as BI, are likely to underestimate the total benefits by the portion that knowledge workers will appropriate for themselves through their opportunistic behavior.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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