Bibliographic record
Abstract
Archivists are depicted in various forms of media, and these representations influence how the world perceives the profession. This study, building on previous research, investigates how archivists are portrayed in film. The authors identified 43 films featuring archivists and conducted a content analysis of each film. The study reveals the lack of a clear image of archivists, who are presented as complex and sometimes ambiguous individuals. Many characters exhibit multifaceted personalities, and few fit the stereotypical qualities identified in previous research studies. This lack of a defined image may stem from a dearth of understanding of the archives profession among both the general public and individuals in the film industry. This poses a significant threat to the profession because it could lead to a lack of funding and an inability to attract donors, researchers, and future archivists. The archives profession must respond to these misconceptions and ambiguous images by engaging in proactive and consistent outreach. The search for a clear identity for archivists is ongoing. It offers an opportunity to generate discussion about the profession within the archival community and to make our complex professional identity accessible to the greater community.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".