Bibliographic record
Abstract
Purpose This exploratory study examined backlash in the workplace. Backlash was operationalized by employee views on how much their employer had done to support the advancement of four designated groups (women, disabled, aboriginal people, racial/visible minorities): too much, about right, too little. Design/methodology/approach Data were collected from 2,514 employees of a single financial services organization (1,962 women, 480 men) using anonymous questionnaires. Findings The majority of the sample thought their employer had done about the right amount. Women thought the firm had done less for women than men did; men thought the firm had done less for aboriginals than women did. Males more strongly endorsing backlash had longer company tenure and tended to be at lower organizational levels. Women and men endorsing backlash were then compared on a variety of work and organizational outcomes. Men believing the firm had done too much, and women believing the firm had done too little generally indicated less satisfying work and organizational outcomes. Research limitations/implications Study needs to be replicated in other organizations using a different measure of backlash. Practical implications Suggestions for dealing with backlash are offered. Originality/value Examines a relatively important but under‐researched subject.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".