Automated Linkedin Analysis to Determine Psychometric Characteristics of a Client
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
The purpose of this paper is to study programs for identifying the psychometric characteristics of a client based on his LinkedIn profile, test these programs and compare their functionality. We studied 52 programs for analyzing profiles of the social network LinkedIn. Only two of these programs have the functionality of psychometric personality analysis. In-depth testing of both programs was carried out. The article presents a comparison of the results obtained by two psychometric analysis programs and provides conclusions about the reliability of the assessments. We can not only determine psychological qualities, but also understand what model of behavior our potential client has, what decisions and how prefers to make, what qualities sympathizes with in others. This approach can be simplified and automated for business thanks to programs based on artificial intelligence. The use of programs that allow you to identify the psychometric characteristics of a client based on his profile on the LinkedIn social network will make it easier to study the target audience, build a communication strategy and promotion strategy, and will be useful for any business.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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