Into the Unknown: Onboarding Early Career Professionals in a Remote Work Environment
Bibliographic record
Abstract
This paper explores the impact of the COVID-19 pandemic from the perspective of three individuals, all of whom are early-career professionals: Julia, a term librarian for an academic library who began her role as the pandemic was causing widespread change; Christine, a recent graduate who started her job search during the pandemic; and Kevin, a current Master of Library and Information Science student who started and completed his co-op in an entirely remote setting. This paper explores their perspectives on job precarity in a remote work environment and provides reflections on working in a library setting during the pandemic. To bring together the key themes experienced throughout this period, several recommendations are offered to managers and early-career librarians as they navigate this new landscape. For employers, advertising new employees, organizing their onboarding, and ensuring concerted efforts for introductions are recommended. For new librarians, forming communities of practice and building relationships in the remote work environment to battle feelings of isolation and not belonging are recommended. The precarious roles most early-career librarians find themselves in is unlikely to improve during the pandemic. The perspectives and reflections shared in this paper are intended to provide a transparent view into the experiences of three early career librarians, what they have learned, and how they are maximizing their time in the remote work environment.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.047 | 0.025 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".