“Putting It into Practice Is the Best Way to Really Learn Something”: Evaluating the North American Graduate Archival Education Curriculum1
Bibliographic record
Abstract
ABSTRACT Despite the vital role graduate archival education plays in preparing aspiring archivists for practice, we lack empirical evidence concerning its efficacy. This study is based on a survey of 406 students and new professionals (SNPs) (five or fewer years in the field). It addresses the following research question: based on their graduate education, how well prepared for practice are SNPs in North America? To address this question, the authors explore the topics SNPs suggested were most useful, the assignments they found most fruitful, their perceptions of topics lacking adequate coverage, their overall rating of their professional preparation's effectiveness, and the ways in which they thought professional preparation might be improved. Notably, SNPs lauded the payoff of experiential learning. They also foregrounded topics related to the 2016 “Guidelines for a Graduate Program in Archival Studies” (GPAS) clusters Arrangement and Description and Digital Materials Management. SNPs suggested that improved professional preparation could come from augmented course offerings, enriched course content, and most important, more hands-on/practical experience. SNPs were vocationally preoccupied; they showed only modest interest in “archival thinking.” The authors juxtapose their findings with both GPAS and A*CENSUS II and suggest that communities of archival practice (COAPs) are a profitable direction for future archival pedagogy.
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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".