Development of New Information Systems with the Involvement of Artificial Intelligence for the Men and Women’s Work: A Methodical Approach to Assessment and Selection of the Optimal
Bibliographic record
Abstract
The main purpose is to create a new effective methodical approach for assessing the selection of the optimal information system with the involvement of artificial intelligence to enhance the efficiency of men and women's work in organizations.The object of study is information systems used in the work of organizational activities in various companies.The research methodology involves the use of the BOCR method, which allows evaluating different alternatives in the development of information systems based on various comparison criteria.As a result of the conducted research, key criteria and two forms of information systems for comparison were presented.The evaluation results showed that according to the BOCR criteria, the most optimal information system is the one that uses artificial intelligence solely for analytical processing without data collection and analysis.The innovativeness of the research results is revealed in the proposed approach to evaluation and comparison.The study has limitations in the form of not considering the specificity of differences in the work of men and women in the context of thinking.Future research prospects should focus on gender issues in evaluation.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".