Sharing Information on Employment Conditions in Social Media by Representatives of Different Generations, and the Image of the Organization
Bibliographic record
Abstract
Social media is becoming an increasingly popular source of information for Internet users. They set up their accounts on the well-known and most frequently used social networking sites in order to use them, inter alia, to exchange information on professional matters. By posting your opinions and comments about the employer, various photos or videos from the workplace, they have a positive or negative impact on the creation of the company's image.The article aims to identify the users' activity in social media in terms of sharing information about their workplace. The article presents the results of the research on: verification of the situations that determined the involvement of the respondents in the publication of negative opinions about the employer; identifying the motives for posting information on working conditions; specification of the types of entries from the company's life that affect its image.This article is an attempt to answer the question whether belonging to a specific generation group and the professional status of an employee influence the generation of positive or negative actions in social media, which translate into the company's image. The considerations carried out as part of the article were based on literature studies and the analysis of the results of surveys conducted in the fourth quarter of 2021 on a group of 530 people (representing 3 generations) from the Śląskie Voivodeship in Poland. The Baby Boomers generation did not take part in the study.
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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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".