Professional Competences of Specialists in Purchasing as a Factor of Purchasing Activity Quality
Bibliographic record
Abstract
One of the top priority directions of the social and economic development of every country is the area of purchasing. The quality of purchasing depends on many factors. Herewith, one of the key factors is the level of the customers’ professionalism. The customers’ professionalism as one of the basic principles of the contractual system requires the development of the complex of professional competences that allow not only to estimate the level of professionalism of any specialist who makes purchases but also to substantiate criteria and indicators of professional competences formedness. Foreign and Russian experience of the customers’ professionalism development allows to single out and substantiate the suggestion that the focus, when training specialists in the area of purchasing, has moved from the simple educational process to the planned purposeful formation of high professional competences of graduates of programs as a whole and every academic discipline in particular. The formation of professional competences is a complicated process based on the integration of the complex of factors that allow the specialist to acquire new professional qualities. The authors prove the necessity to make changes in the notion “the customers’ professionalism” and consider it as a multiple professional and personal feature that contains motivational, cognitive, personal, activity, and reflexive components.
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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".