Emotional responses in archival work
Bibliographic record
Abstract
Building on previous work investigating the impact of exposure to (a) records with traumatic potentialities and (b) interactions with donors and community researchers whose suffering is documented in the archives, this study sought to better understand emotional aspects of archival work. Using a diary research methodology, 15 archivists engaged in diary keeping for approximately four months. What emerged was a broad set of events and experiences that triggered a wide range of emotional responses arising from archival work. This included: pre-existing emotional states and characterological traits; emotional exchanges in the workplace with colleagues and others; emotional demands of the work (including emotion work and emotional labour); team and leader interactions arising from group tasks and leader behaviour; and organizational policies, climate, resources and demands. This broader set of interactional factors forms the foundation on which traumatic and other troubling events are encountered. Future research must consider the nature of archival organizations and interactions within them that contribute to the overall working experience. In addition, archival organizations need to take responsibility for creating a culture that demonstrates respect and appreciation for workers, acknowledges the interpersonal challenges of the work, and provides supports for archivists who are shouldering the challenges.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".