Physicists and Astronomers Use Google as a Starting Point for Specific Queries, but Do Not Intentionally Use It to Search for Articles
Bibliographic record
Abstract
A Review of: Jamali, H. R., & Asadi, S. (2010). Google and the scholar: The role of Google in scientists' information seeking behaviour. Online Information Review, 34(2), 282-294. Objective – To determine how Google’s general search engine impacts the information-seeking behaviour of physicists and astronomers. Design – Using purposive stratified non-random sampling, a mixed-methods study was conducted which included one-on-one interviews, information-event cards, and an online questionnaire survey. Setting – Department of Physics and Astronomy at University College London. Subjects – The researchers interviewed 26 PhD students and 30 faculty members (23% of the department’s 242 faculty and students), and 24 of those participants completed information-event cards. A total of 114 respondents (47.1% of the department members) participated in the online survey. Methods – The researchers conducted 56 interviews which lasted an average of 44 minutes each. These were digitally recorded, fully transcribed, and coded. The researchers asked questions related to information-seeking behaviour and scholarly communication. Four information-event cards were given to volunteer interviewees to gather critical incident information on their first four information-seeking actions after the interview. These were to be completed preferably within the first week of receiving the cards, with 82 cards completed by 24 participants. Once initial analysis of the interviews was completed, the researchers sent an online survey to the members of the same department. Main Results – This particular paper examined only the results related to the scholars’ information-seeking behaviour in terms of search engines and web searching. Details of further results are examined in Jamali (2008) and Jamali and Nicholas (2008). The authors reported that 18% of the respondents used Google on a daily basis to identify articles. They also found that 11% searched subject databases, and 9% searched e-journal websites on a daily basis. When responses on daily searching were combined with those from participants who searched two to three times per week, the most popular method for finding research was by tracking references at the end of an article (61%). This was followed by Google (58%) and ToC email alerts (35%). Responses showed that 46% never used Google Scholar to discover research articles. When asked if they intentionally searched Google to find articles, all except two participants answered that they do not, instead using specific databases to find research. The researchers noted that finding articles in Google was not the original intention of participants’ searches, but more of a by-product of Google searching. In the information-event card study, two categories emerged based on the kinds of information required. This included participants looking for general information on a specific topic (64%, with 22 cases finding this information successfully), and participants knowing exactly what piece of information they were seeking (36%, with 28 cases finding information successfully). There was no occurrence of using Google specifically to conduct a literature search or to search for a paper during this information-event card study, although the researchers say that Google is progressively showing more scholarly information within its search results. (This cannot be ascertained from these specific results except for one response from an interviewee.) The researchers found that 29.4% of respondents used Google to find specific pieces of information, although it was not necessarily scholarly. Conclusion – Physics and astronomy researchers do not intentionally use Google’s general search engine to search for articles, but, Google seems to be a good starting point for problem-specific information queries.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".