Information Seeking Behaviors, Attitudes, and Choices of Academic Chemists
Bibliographic record
Abstract
Chemists in academic institutions utilize a variety of resources and strategies to remain current and to track scholarly information, patents, and news. To explore how chemists in academic institutions remain current, librarians at four Canadian university institutions surveyed 231 and interviewed 14 chemistry faculty, staff, and graduate students on their information seeking behaviors and attitudes. According to survey results, a minority of chemists (13.9 percent) acknowledged that they were successfully keeping up to date, while 50.6 percent indicated that they were somewhat successful. However, a significant number of chemists (35.5 percent) indicated that they were unsuccessful and could do better in remaining current with information. Investigators analyzing focus group data identified three emergent themes related to remaining current: (1) there is “too much information – and not enough time.” No single information seeking strategy works; (2) “patents are important – but messy.” Chemists find themselves largely suspicious about the value and credibility of patents; and (3) chemists “could do better” in keeping up to date with new and emerging technologies. Chemists continue to be open to new tools and resources yet readily acknowledge that they are too often not sure which information seeking behaviors, resources, or strategies work best. This study helps to shed light on opportunities to identify and meet chemists’ evolving information 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".