Are e‐books replacing print books? tradition, serendipity, and opportunity in the adoption and use of e‐books for historical research and teaching
Bibliographic record
Abstract
This article aims to understand the adoption of e‐books by academic historians for the purpose of teaching and research. This includes an investigation into their knowledge about and perceived characteristics of this evolving research tool. The study relied on Rogers's model of the innovation‐decision process to guide the development of an interview guide. Ten semistructured interviews were conducted with history faculty between October 2010 and December 2011. A grounded theory approach was employed to code and analyze the data. Findings about tradition, cost, teaching innovations, and the historical research process provide the background for designing learning opportunities for the professional development of historians and the academic librarians who work with them. While historians are open to experimenting with e‐books, they are also concerned about the loss of serendipity in digital environments, the lack of availability of key resources, and the need for technological transparency. The findings show that Rogers's knowledge and persuasion stages are cyclical in nature, with scholars moving back and forth between these two stages. Participants interviewed were already weighing the five characteristics of the persuasion stage without having much knowledge about e‐books. The study findings have implications for our understanding of the diffusion of innovations in academia: both print and digital collections are being used in parallel without one replacing the other.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".