Implementation of multi-criteria decision making approach for the team leader selection in IT sector
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
In the era of technology, the demand of the software development increases at a very high speed, as software has touched the human's life in all aspects. The better quality software development acquiring minimum development time leads to the team work in which a group of people has been formed that work together in a team for the software development. One of the most significant issues in effective and efficient teamwork is the team leader selection because the team leader is the person in any team that is going to handle all types of managerial activities such as leadership, motivation to others, etc. The team leader selection process may be dependent on numerous conflicting selection indexes that make it a Multi-Criteria Decision Making (MCDM) problem. In the present research, an MCDM approach namely, Euclidean Distance Based Approximation (EDBA) which is based on the calculation of the composite distance value for each alternative from a hypothetical optimal point is presented. The result of this study provides a comprehensive ranking of team leaders that leads to the right selection of team leader in information technology (IT) sector.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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