A mathematical and testing tool for personal human capital research assessment
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 article establishes and grounds the topicality of personal human capital for its development. It proves that the available approaches, methods and models do not consider the business enterprise needs to increase the efficiency of individual employee's contribution to the formation of market value. Human capital is offered to consider as a function of personal intelligence, which depends on the personal creative, status and social abilities. The adaptation of mathematical tools is conducted to establish intellectual personal characteristics with justification of different variants of their numerical values. The article also completes the evaluation tools by way of questionnaires, tests and personal objective characteristics, and suggests the approaches to the procedure of results evaluation and interpretation. The testing of the mathematical and testing tools of the personal human capital research was conducted with a group of people whose team is as close as possible to a typical industrial staff workers or its structural subdivision. The results of the personal human capital survey allowed us to identify both individual and collective needs in improving the planning processes for its development.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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