Sharing Information on Employment Conditions in Social Media by Representatives of Different Generations, and the Image of the Organization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it