"I Genuinely Can’t Understand Why I Was Selected for the Job": Descriptions of the Impostor Phenomenon in University Staff and Professors
Bibliographic record
Abstract
The Impostor Phenomenon (IP) is a person’s experience of internalized fraudulence relative to their successes; this is characterized by a fear of being “found out” and judged by others as well as difficulties internalizing successes (Clance & Imes, 1978). Previous research has suggested that it is commonly experienced by professors and staff in postsecondary institutions and associated with a variety of negative outcomes related to career and mental health. As part of an online survey about IP, academic staff and professors were provided an open text box and asked to describe the causes, consequences, and experiences of impostor feelings in academic settings. Three overarching categories and eight subcategories were identified through inductive content analysis, including 1) triggers of impostor feelings (i.e., interpersonal interactions, situational influences), 2) qualities of the experience itself (i.e., negative external perceptions, negative self-perceptions, feelings of fraudulence, negative emotions) and 3) management of impostor feelings (i.e., effective strategies, ineffective strategies). Implications for addressing impostor feelings in academic staff and professors are considered.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".