Seeking knowledge: An exploratory study of the role of social networks in the adoption of Ebooks by historians
Bibliographic record
Abstract
Abstract Despite their initial slow diffusion in society, Ebooks have recently garnered renewed interest from academics as part of a move toward the digital humanities. To examine how humanists are adopting Ebooks, we focus on the first stage, the Knowledge Phase, of Rogers' model of the diffusion of innovations. Central to this stage is the study of adopter attitudes toward the innovation and the role played by social networks in the adoption process. Historians were selected as the population of study because of their close relationship to the printed book, both as a research tool and as an academic goal. Six semi‐structured interviews were conducted with historians and then analyzed using a grounded theory approach. Our preliminary results show that historians had both positive and negative attitudes towards the Ebook. Often the same person showed eagerness and curiosity to adopt certain features of Ebooks whilst showing some degree of reluctance and skepticism. We identified the Role of the Social Network (RSN) as an important factor in the decision‐making process of historians. Respondents frequently mentioned the subject specialist librarian for history as a key source of information. In addition, historians went not only to peers inside of the department, but also to friends and colleagues elsewhere when seeking advice on working with Ebooks. As Ebooks gain ground within academia, studies such as this, that focus on a single discipline, will be necessary to understand why scholars make the decision to adopt or reject. The study results found that subject librarians can act as change agents on the university campus. For this reason, the impact they have on the use of new technologies by academics needs further attention.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".