The role of agency in historians’ experiences of serendipity in physical and digital information environments
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the changing research practices of historians, and to contrast their experiences of serendipity in physical and digital information environments. Design/methodology/approach In total, 20 historians in Southwestern Ontario participated in semi-structured, in-depth interviews. The interviews were transcribed and analyzed employing grounded theory. The analytical approach included memoing, the constant comparative method, and three phases of coding. Findings Four main themes were identified: agency, the importance of the physical library experience, digital information environments, and novel heuristic forms of serendipity. The authors found that scholars frequently used active verbs to describe their experience with serendipity. This suggests that agency is more involved in the experience than previous conceptualizations of serendipity have suggested, and led us to coin the term “incidental serendipity.” Other key findings include the need for digital tools to incorporate the context surrounding primary sources, and also to provide an organizational context much like what is encountered by patrons in library stacks. Originality/value The increased emphasis on digital materials should not come at the expense of the physical information environment, where historians often encounter serendipitous finds. A fine balance and a greater integration between digital and physical resources is needed in order to support scholars’ continued ability to make connections between materials. By showing the active role that historians take in their serendipitous encounters, this paper suggests that historical training is critical for eliciting incidental serendipitous encounters. The authors propose a novel approach, one that examines verbs in serendipity accounts.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.011 | 0.007 |
| 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".