Identificación de los principales factores de rotación de los millennials de puestos administrativos en empresas de Lima metropolitana
Bibliographic record
Abstract
Abstrac: Once and for different reasons the word millennials have been heard and read in different fields, whether personal, professional or academic. The members of this generation stand out for their adaptation to change, their high self-esteem, their familiarity with technology and their contribution with new ideas about a particular activity. This generation currently represents a quarter of the Peruvian population, which is equivalent to eight million inhabitants, who have very particular interests in relation to work, quality of life and personal development. Their relationship capacity is different from that of other generations, they are pending the latest communication trends; Connectivity is part of your being, it is the generation of speed, from the here and now. However, being always aware of new technologies and new processes mean that millennials do not feel comfortable in an organization that does not challenge them especially when they have been working for more than two or three years. Given this, labor turnover is a problem that all organizations worldwide must face, even more, large companies, where most of their employees are represented by this generation. These companies must ask themselves what or what are the reasons why this staff rotates from work to work, but not before recognizing in their organization the true millennial to know the type of leadership that the bosses must apply to have them and retain them within their team. The time for the millennial generation is worth a lot, largely because they have grown up watching their parents and grandparents dedicating most of their lives to work, their families have lived to work and this generation seeks to change this concept, based on the balance between Work and its quality of life. This research work will search through research, surveys, interviews, tables, theories and statistical tables to know what are the main reasons why this group tends to change companies in the short term and what actions organizations should take to retain them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".