A Mathematical Approach to Evaluating Managerial Skills: Economic Cybernetics and the Convex Operational Field
Bibliographic record
Abstract
This paper presents a novel methodology for quantifying managerial competence through the lens of economic cybernetics.The proposed model is premised on the definition of a convex operational field, wherein the limiting economic conditions are delineated by operational management (MO), strategic management (MS), and predictive management (MP) skills.Any given point (representative of an economic situation) within this convex operational field is attributed to a successful manager, whose leadership abilities are proportionally expressed depending on their position.It is posited that the adeptness of a successful manager inherently shapes the criterion function, while the boundaries of the convex operational field define the specifications of this function.The convex operational field is examined through two lenses: the identification of managerial skills and the calculation of the elasticity coefficient.The case study presents the convex operational field as defined by the economic conditions of MO, MS, and MP.This analysis can be applied to both linear and non-linear programming, thereby providing avenues for the application of advanced mathematical methodologies in resolving economic and managerial challenges.
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.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".