Measuring the dynamic engagement with a system of equations – Theory demonstration and initial analysis
Bibliographic record
Abstract
Employees' engagement(EE) is a conventional theme in every human resource department and due to that, several methods are available to cope with its importance. There are two areas related to EE: one to increase or sustain the engagement, and other to measure or classify the engagement level. This work aims to contribute in both areas. To test this the authors, propose a measurement of employee's engagement involved in the continuous improvement project. Due to the work explores the present methods in the market and proposes a new method. Differently, from the existent, the one proposed in this work consist of a system of ordinary differential equations to understand shed more light in the EE. Also, introduce the methodology to measure the dynamic engagement, it means the real engagement level. To base our research the authors used the classical Lokta-Volterra model, also known as Predator-Prey Model. Consequently, the model aims to simulate the future state of the engagement and providing a superior notion of the necessary amount of time needed for continuous improvement. To present the method, the work proposes a balance between the two moments present (but not always measured) in every company during work time: The volume of overtime or wasted time and the time dedicated to improve the process. The previous results present in this work show that the predator-prey model can be adapted to measure the impact of continuous improvement on employee engagement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".