“I can't work part-time for the rest of my life”: Students, Early Career Professionals, and the Uncertain Prospects of an Archival Career
Bibliographic record
Abstract
ABSTRACT In 2004, the A*CENSUS survey identified several challenges related to the future of the archival field, including enhancing the recruitment, training, and retention of archivists. The A*CENSUS II survey conducted in 2021 shows that the archival field has grown and grown younger in the intervening period, but recruitment constitutes only one part of a robust archival enterprise; retention depends upon ensuring rewarding career experiences. This exploratory research examines archival students’ and early career professionals’ perceptions of the prospects of developing an archival career. Drawing on a survey of 406 students and early career professionals (five or fewer years in the field), the authors examine topics of credentials, career paths, professional development plans, and attrition. Findings indicate overall satisfaction with their experiences, though concerns were raised regarding preparation for entering the field; employers’ reception of transferable skills; the ability to secure full-time, well-compensated positions; and the perception that success requires multiple degrees and credentials. The authors discuss the implications of these findings for practice and provide directions for further research.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".