Public Employer in the Labor Market - the Armed Forces in Public Perception
Bibliographic record
Abstract
Public organizations, including the armed forces, like other employers, are looking for candidates for soldiers and military personnel in the labor market. Taking into account the attributes that make up the attractiveness of an employer and their interdependence, a study was carried out in the course of which the aim was to find out how one of the public organizations, the armed forces, is perceived by people who may become soldiers or who may take up a job in the military how the sources of information through which it reaches them are assessed, and which of its features may attract their attention. The goal was achieved by carrying out, in the first quarter of 2022, a nationwide survey of people between the ages of 18 and 44. Aiming to better understand the opinions of respondents, an analysis of the labor market situation in all voivodeships was carried out. To analyze the collected data and present the results, Statistica v.13.3 and R/RStudio software were used. As a result of the survey, it was possible to establish that the armed forces enjoy a high level of trust among citizens. Respondents assessed the qualities and motives, tangible and intangible, that may encourage service and work in the military due to the potential benefits for candidates. From the perspective of the armed forces, indications as to individual elements can be the basis for improving personnel policies and marketing communications.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".