Opportunity Knocks: Perceptions of Fairness in Employee Benefits
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
This article considers potential conflicts between the principles of equity, equality and need in perceptions of fairness regarding employee benefits, based on self-interest bias, and makes specific predictions regarding perceptions of distributive justice in specific benefit plans. It includes predictions regarding perceptions of procedural justice. A survey of 497 employees in seven Canadian organizations tested the predictions. Findings indicate that need is still an important criterion for assessing distributive justice in employee benefits, although the survey also found evidence of self-interest bias. Perceptions of procedural justice were found to be significantly higher in plans with extensive communication and employee participation in plan design. Organizations that take a proactive approach to understanding how employees determine their perceptions of procedural and distributive justice in employee benefits, and design a benefit plan accordingly, can potentially increase employees’ perceptions of justice regarding employee benefits and reap associated benefits including improved employee retention, enhanced ability to hire and increased benefit satisfaction.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it