Failure to Launch: Feelings of Failure in Early Career Librarians
Bibliographic record
Abstract
This article is adapted from a lightning talk given at the New Librarian Symposium 3.0 held in Toronto, Ontario on Friday June 15th 2018. The feeling of being a failure is something that has been familiar to us at various points in our LIS careers. By exploring our personal narratives of failure, we look more critically at our understanding of failure and how it works within the broader context of our profession and professional identities. This exploration revealed that our experiences of failure are heavily influenced with systemic structures, including institutional and societal pressures, professional norms, and broader neoliberal and capitalist ideas. Failure has a tendency to be something that we regard inwardly and carry individual responsibility for; we want to encourage readers to look beyond themselves as a source of failure and instead at the structures and systems that influence our work and understanding of failure.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".