Profile, needs, and expectations of information professionals: What we learned from the 2003 ASIST membership survey
Bibliographic record
Abstract
Abstract A survey of American Society for Information Science and Technology members was administered via the Web in May 2003. The survey gathered demographic data about members and their preferences and expectations in regard to conferences and other ASIST products and services. With about a 32% return rate, findings were compared with an earlier survey conducted in 1979, which provides a glimpse of how the Society has changed and what needs to be done to ensure a healthy future development. The gender split has remained the same but members are about 5 years older on average than they were in 1979. A significant shift has occurred in members' institutional affiliations, from the largest group being in the industrial sector to the largest group being in educational institutions. Members on average reported slightly higher incomes (after adjusting for inflation) in 2003 than in 1979. Since 1979, a larger percentage of members have earned a doctoral degree. The most common field of study is library and information science. About half of the respondents reported that ASIST is their primary professional society. Their primary reason for maintaining ASIST membership is “learning about new developments/issues in the field.” The most common responses to the question about what factors would make ASIST conferences more appealing related to lowering costs. Other responses related to attitudes about the ASIST Bulletin and the value of other proposed products and services are summarized and reported. Detailed analyses of relationships among different variables made possible a deeper understanding of members' needs and expectations, which provides directions for design of programs and services.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.035 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".