Information Seeking Behaviors, Attitudes, and Choices of Academic Physicists
Bibliographic record
Abstract
Physicists in academic institutions utilize a variety of resources and strategies to seek, find, and use scholarly information and news. Using a sample of physicists, researchers surveyed 182 students and faculty at seven Canadian university institutions to explore self-perceived success rates, resources consulted, databases used, and use of social media and citation management systems. To complement the survey, 11 follow up interviews/focus groups were completed with participants to further uncover information-seeking behaviors, choices, strategies, and feelings around keeping up to date with information needs. According to survey results, a minority of physicists (15.4%) acknowledged that they were successfully keeping up to date. However, a significant number of physicists (28.6%) indicated that they were unsuccessful and could do better in remaining current with information needs. Co-investigators, using qualitative analyses, identified four emergent themes: (1) There are “too many papers – and not enough time” to effectively search, evaluate and read scholarly papers of interest; (2) Staying up to date is important especially in competitive research areas; (3) Graduate students seek information differently than faculty and experienced researchers; and (4) The arXiv database is important to many physicists. Additional minor themes included physics-related publishing is constantly evolving; physicists use a variety of information-seeking behaviors; and, information-seeking methods can differ between physics subdisciplines. This study aims to shed light on opportunities for academic librarians to identify and meet physicists’ evolving information behaviors, attitudes, choices, and needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".