Behavioral Mapping, Using NLP to Predict Individual Behavior : Focusing on Towards/Away Behavior
Bibliographic record
Abstract
“What candidates or team members will do in specific circumstances” has always been an important piece of information for most employees or team leaders to consider when making a decision. Different companies take a significant amount of time to determine who is the best candidate for a particular job position. Companies are looking for the most efficient method for making this decision. Most of the time, personality assessments are used to identify an individual’s character traits. Regardless of personality type, individuals will behave differently in a positive atmosphere than in a stressful one. Hence, characteristics alone can not predict behavior. Thus, text analysis and the identification of candidate behaviors (behaviorism) now enable companies to understand how people think, feel, and act in a given situation and then choose from a vast pool of candidates the best candidate for the job. By leveraging the existing intellectual property data associated with behavioral mapping in AccuMatch Behavior Intelligence as well as expert data and using tools such as Amazon comprehend service (ACS), IBM Watson Natural Language Understanding (NLU), and Machine Learning (ML) techniques, five different methods have been provided and analyzed to predict how an individual in a team gets motivated. Therefore, this paper presents multiple proposed methods to predict Towards/Away behavior and discusses the rationale behind the selection of these methods along with the results obtained.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".