An Action Research on the Introduction and Legitimization of Toilet Cleaning Activities in One Japanese Company
Bibliographic record
Abstract
Although the importance of cleaning and toilet cleaning has been practically pointed out for a long time in Japan, the relationship between cleaning and corporate management has never been academically examined in Japan, let alone in other countries. In this study, through several years of action research, we present the process of introducing and continuing toilet-cleaning activities in one Japanese company. We clarify how the practice of cleaning toilets, which has a long history for many Japanese companies, but is a new practice for this company, is socialized within the organization. In particular, we clarify how the practice of toilet cleaning shakes organizations and people, and how the practice is legitimized within the organization. We will then explore how the results of the survey and the findings of the survey can be positioned academically. Specifically, we will examine whether toilet-cleaning activities can be positioned as a social practice in the context of management studies based on practice theories. In addition, in the context of organizational change theory, we will examine whether toilet cleaning activities can be positioned as a starting point for organizational innovation. We then aim to present the academic implications for practice-based management studies and organizational change theory.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".