A comparison of two methods for studying emotional responses to archival work: Remote interviews and diaries
Bibliographic record
Abstract
Introduction. This paper reports on the use of two research methods to gather data from archivists on their emotional responses to archives and to document the experiences and emotions of archivists. It also draws on comments from the eight archivists who participated in both studies. Method. The research involved semi-structured, hour-long virtual interviews; solicited diaries; and monthly check-ins and exit interviews with diary-keepers. Analysis. The research team read, analysed, and coded the diary and interview data, as well as the notes from the check-in and exit interviews. We subjected the data to line-by-line analysis and coding. Emerging themes and patterns were identified and categorized, and interrelationships were then determined in an iterative and reflexive manner. Results. The study identified four major distinctions between the two methods. First, the interviews provided deeper insights into significant events, while diaries reflected day-to-day documentation of micro-events and emotions. Second, the interview script focused on negative emotions, whereas diaries captured a broader range of emotions. Third, interviews provided a more distant temporal perspective on past events, while diaries were time-dependent,. Finally, power different imbalances were inherent within each methodological approach. Conclusion. The use of different methods provided different insights into the emotional responses to archives and their causes as well as creating a different experience for the interviewees and diary-keepers.
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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 | 0.063 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".