“I don’t belong here!” Imposter Feelings – the threat is real for women in leadership!
Bibliographic record
Abstract
Do you ever feel that you don ’ t deserve your success? Do you feel like you are waiting forsomebody to tap you on the shoulder and tell you: ‘ you don ’ t belong here ! ’ You may besuffering from impostor feelings (syndrome). Also referred to as ‘ impostor phenomenon ’ or ‘ fraud syndrome ’ , this is a term used to describe the psychological experience of feeling likeyou don't deserve your success. \nIn a recent study 1 conducted at Heriot-Watt University in Scotland, it was found that 36% ofleaders experience frequent or high levels of ‘ impostor feeling ’ (IF). Female leadersexperience IF to a higher degree than men. In this study, 54% of women scored frequent orhigh versus a quarter (24%) of men. \nIn this interactive session, delegates will learn that although not a diagnosable mental healthcondition, feeling like a fake can be linked to anxiety and depression. We will explore whyimpostor syndrome tends to be more common in women, and in those who are highachievers. \nDelegates will also have an opportunity to undertake a quiz to explore their level of imposterfeelings, and we will examine strategies about what can be done about this negative self-talkand potential self-sabotage as female leaders.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".