Navigating Imposter Syndrome: A Workshop for Emerging Librarians
Bibliographic record
Abstract
InspirationHow did I get into this program?I'm the only one not following this lecture.Don't ask that question, it's stupid!Thoughts like these have plagued us since we entered Western University's Master of Library and Information Science (MLIS) program in January 2017.We have consistently overlooked our skills, dismissed our successes, and fallen victim to "Imposter Syndrome (IS)"a term coined by Dr. Pauline Rose Clance and Suzanne Imes (1978).The term has since been adapted by John Harvey and Cynthia Katz (1985), and it is now taken to mean:1.A belief that one has fooled others into overestimating one's own abilities; 2. Attribution of personal success to factors other than one's ability or intelligence; and 3. Fear of exposure as an imposter.Consistent with the many who "go to great lengths to hide their 'fraudulent' feelings" (Harvey & Katz, 1985), each of us went through the early months of our MLIS program experiencing IS in silence.At that time, we believed our feelings of fraudulence were unique, but after later sharing our misgivings with peers, we came to recognize that IS is widespread across Western's MLIS program.If existing research is any indication, IS will persist even once we begin our careers as librarians.Melanie Clark, Kimberly Vardeman, and Shelley Barba (2014) studied IS among college and research librarians, revealing that one in eight librarians provide the tools needed to overcome IS before it has already surfaced as a barrier to one's early career success.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".