Managing a perilous stigma: Ex-offenders’ use of reparative impression management tactics in hiring contexts.
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
Individuals with a criminal record face employment challenges because of the nature of their stigma. In this study, we examined the efficacy of using reparative impression management tactics to mitigate integrity concerns associated with a perilous stigma. Drawing on affect control theory, we proposed that the use of 3 impression management tactics-apology, justification, excuse-would differentially affect hiring evaluations through their influence on perceived remorse and anticipated workplace deviance. Across 3 studies, we found support for our proposed model. Our results revealed the use of an apology or justification tactic when explaining a previous criminal offense had a positive indirect effect on hiring evaluations, whereas the use of an excuse tactic had a negative indirect effect. These findings suggest applicants may benefit from using impression management tactics that communicate remorse when discussing events or associations that violate integrity expectations. (PsycINFO Database Record
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it