УпÑавление кадÑовÑм ÑезеÑвом (на пÑимеÑе ЯÑкинÑкого гоÑÑдаÑÑÑвенного паÑÑажиÑÑкого авÑоÑÑанÑпоÑÑного пÑедпÑиÑÑÐ¸Ñ ÐемеÑовÑкой облаÑÑи)
Bibliographic record
Abstract
Final qualification work contains 97 pages, 13 drawings, 18 tables, 43 used sources.\nKeywords: SHOTS, the PERSONNEL RESERVE, HIRING of PERSONNEL, the SCHEDULE â the CALENDAR, ROTATION, it is ADMINISTRATIVE â ADMINISTRATIVE PERSONNEL, CATEGORIES of WORKERS, PROFESSIONAL DEVELOPMENT, TRAINING\nObject of research is the Yashkinsky state motor transportation passenger enterprise of the Kemerovo region. \nThe work purpose - elaboration of strategy of management of a personnel reserve and search of ways for her development.\nIn the course of research research of a personnel component of the studied organization was conducted; analysis of personnel potential of workers; system of the existing personnel reserve.\nAs a result of research actions for development of the areas of work with a personnel reserve have been developed. \nExtent of introduction: partially actions have been introduced in practice of Yashkinsky GPATP in 1 quarter 2016.\nScope: personnel management.\nEconomic efficiency / importance of work consists to statement of system of work with a personnel reserve and development of the uniform concept to realization of this direction in the studied organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.009 | 0.009 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.016 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.028 |
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; both teacher heads agree on what is shown here.
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".