Bibliographic record
Abstract
Uncomfortable with the transition from “learner” to “expert”, many new course instructors question whether they are capable of performing their role (Craddock et al., 2011; Parkman, 2016). This workshop delves into the “Impostor Phenomenon,” a term used to describe feelings of incompetence despite evidence of competence. In general, the literature suggests that greater awareness of the Impostor Phenomenon, both in oneself and others, is the first step towards breaking the cycle of behaviour (Clance & Imes, 1978; Hutchins, 2015). To foster this increase in awareness, this workshop encourages participants to engage in self-reflection and discussion to help break down the barriers normally associated with thoughts of perceived weakness or failure, and normalize these feelings. Participants have the opportunity to reflect on their own experiences with the Impostor Phenomenon, as well as those of their students, and discuss different strategies that can be applied to minimize impostor feelings in the classroom.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".