Understanding Group Differences and Predicting the Impostor Phenomenon Among University Staff and Faculty
Bibliographic record
Abstract
The Impostor Phenomenon (IP) is defined by an individual’s experience of internalized fraudulence relative to their own successes and is characterized by cognitive (e.g., decreased self-esteem), affective (e.g., low mood, anxiety) and behavioural dimensions (e.g., avoidance of job-related challenges). Previous research has demonstrated the prevalence of the IP in academic settings among staff and professors, as well as its deleterious connections with burnout and emotional exhaustion. A sample of 162 academic staff and professors completed measures of the IP, resilience, general self-efficacy, and satisfaction with life. Results indicated higher IP scores among professors and early-career professionals but not in women or individuals who identify as visible or sexual minority group members. Negative correlations were found between the IP and resiliency, work engagement, general self-efficacy, and satisfaction with life. IP scores were predicted by lower general self-efficacy, being a professor, and lower resilience. Implications for wellness among academic staff and professors, as well as the wider institutional climate, are considered.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".