WHAT WORKPLACE PROTECTIONS DO EMPLOYEES HAVE IN US ORGANIZATIONS? A MODERN-DAY ANALYSIS OF DISTRIBUTIVE AND PROCEDURAL JUSTICE SYSTEMS AS A METHOD OF EMPLOYEE EQUITY AND POWER
Bibliographic record
Abstract
The concepts of Procedural and Distributive Justice have been subject to extensive research since it was theorized in the 1970’s in the fields of social and industrial/organizational psychology. The weakness with the majority of the research is the lack of actual employees as the sample populations and used mostly students. Anderson and Ruderman (1987) were some of the original scholars who used actual federal employees in their studies and this research was considered one of the major research studies in the field of Procedural and Distributive Justice. This research has undertaken the concepts of the original study that was conducted by Anderson and Ruderman and created a comparison study that used a similar research design framework. A sample of 521 employees from multiple industries across all geographic locations in the U.S. were obtained. Using the Survey from the Anderson and Ruderman studyallowed for the same questions to be asked today versus 1987 of employees about Justice Systems in their jobs and employment situations. Similar Hypotheses were tested. Results were found to be similar in most cases with some slight deviations. The variances can be potentially attributed to the use of more advanced statistical tools that are available in today’s research than was available and economically feasible for use in 1987. The findings indicate a need for management to provide equal treatment of employees and provide them with greater opportunities for engagement and involvement in their work environment.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".