<研究ノート>働き方の現状と課題 : 『ブラック的』な働き方に関する考察
Bibliographic record
Abstract
The issue of illegal conditions and harassment ignored at the workplace that put employees in dire straits is one that has received much attention in recent years. This paper analyzes the results of a survey conducted on workers (both regular and nonregular) under the age of 35 to determine what working styles are present in their workplaces, particularly to what extent so-called “black” work exists, and how employees who are not yet managers are addressing it. The survey revealed the following: First, when we asked whether their place of employment could be considered a so-called “black company” that uses illegal work hours or treats workers as disposable, 22.1 percent of respondents said yes. This percentage was higher for both regular and non-regular employees at small- and medium-sized companies and for non-regular employees at large companies, compared to regular employees at large companies. Furthermore, there was a clear trend for higher percentages where labor unions were not present. Second, there are many workplace and workingstyle concerns at places employees identified as “black companies,” including too much overtime and difficulty taking time off, a poor environment for employee growth, and little\ncommunication between supervisors and their\nstaff. Third, only 36.9 percent of respondents are taking some kind of action regarding these workplace issues, such as consulting with a supervisor. Of those respondents who believe their workplace is a “black company,” more than half are doing nothing about it. Fourth, one quarter of respondents reported that working long hours had had an adverse effect on their health in the past three years, and 29.7 percent said they were not satisfied with the work at their current place of employment. These numbers were higher for those workplaces identified as “black companies”. Fifth, combined with the 18.2 percent who said a labor union was absolutely necessary and those who said it would be better to have a\nlabor union than not, over half of respondents believe labor unions are necessary. Where respondents considered their place of employment to be a “black company,” the percentage in favor of labor unions increased, showing that much is expected from labor unions with regard to resolving workplace and working-style concerns. These results suggest that issues such as long working hours are cropping up at the workplace; that these issues have an adverse effect on workers’ health and satisfaction levels; and that labor unions, which gather worker opinions and come up with ways to cope with these issues, play an important role.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".