“Sometimes I feel like they hate us”: The Society of American Archivists and Graduate Archival Education in the Twenty-first Century
Bibliographic record
Abstract
ABSTRACT The Society of American Archivists (SAA) has long involved itself with graduate-level archival education. It has sponsored committees and subcommittees, guidelines, roundtables/sections, student chapters, and pre-conferences. But limited empirical evidence exists regarding faculty members' view of SAA's involvement with graduate archival education. This exploratory qualitative case study employs semistructured interviews with full-time, tenure-track archival faculty. We address the ways in which SAA contributes to faculty members' teaching, faculty members' encouragement of students to join SAA, SAA student chapters and faculty advising, and how SAA might promote better communication, coordination, and collaboration between graduate archival education programs and practitioners. We contend that despite decades of effort on both sides, the relationship between graduate archival education programs and the Society of American Archivists remains disjointed, ultimately limiting the field's development. We offer recommendations and suggestions for future research to strengthen this relationship in the interest of improving student experience and the health of the profession.
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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 | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.043 | 0.054 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".