Binary decision automata modelling stress in the workplace
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 study builds on previous work modeling stress in the workplace. It incorporates a new and more sophisticated agent representation called a binary decision automata. Agent training uses inaccurate mimetic behaviour to adopt the successful behaviour of highly productive mentors. There are three tasks an agent can undertake; rest, a base job and a special project. The relative worth of these tasks vary stochastically week-to-week representing the changing priorities of management. Stress is accumulated through working long hours and impacts performance of the agent by decreasing productivity. Covert drug use is implemented into the model through the incorporation of a few individuals with much higher stress tolerance than the base agents. Binary decision automata have substantially greater learning capabilities, reflected in the increased productivity and lower overall monthly firings compared to previous research that used a simple string representation for agents. Moreover, with the inclusion of covet drug use amongst agents, the binary decision automata have the capabilities to learn effective behaviour and adapt to the challenging demands of the high performing drug agent mentors. This is in sharp contradistinction to the string agents.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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