Organizational Management: Quiet Quitting's Mitigation Strategies for Organizational Response
Bibliographic record
Abstract
After the COVID-19 pandemic, the world economy is in a depression and has a high inflation rate, as the unemployment rate gets higher and higher, employment gets lower and lower, young people are very pessimistic about their prospects. Therefore, the employment has become a serious problem in society, which has also caused strong social discontent. Also, all of these factors may lead to a sense of anxiety among today's workers, and it's also accompanied by fatigue, pessimism and insecurity. The status quo of “the rat race” in all fields has become more and more intense under such social conditions. The word “quiet quitting” is widely used by people. The paper will analyze the impact of “quiet quitting” on individuals and organizations and come up with some solutions to reduce “quiet quitting” for organizations, such as job satisfaction and motivation, stress and strains, etc. In addition, this paper will adopt the form of a questionnaire to investigate the data and uses these data to help analyze people's attitudes and idea of the impact of “quiet quitting”.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".