Bibliographic record
Abstract
The development of enabling technologies for data collection has led to an exponential growth in the availability of information, presenting challenges in finding relevant information, organizing it and using it to advantage. But it also offers opportunities to independent information professionals who can leverage their knowledge of the information landscape, of ever changing information search techniques, of information use patterns and of information management technologies to provide services of value to their clients. In this group of articles Cindy Shamel and Liga Greenfield, who work with clients in the biomedical industry, discuss the value and process of helping clients articulate their information needs to locating relevant resources efficiently and the value of analyzing organizational information flows in defining information management solutions. Tom Wolff and Stephen Adams, specialists in patent information, discuss the many ways IIP patent consultants provide business intelligence, support legal opinions and business decisions and provide strategic guidance using patent analytics and services like intellectual property management and database development and training. Jane John, Jocelyn Sheppard and Jan Knight use a question-and-answer format to present their views on getting started in business, reaching and meeting target clients, the challenges of marketing and the unique opportunities and benefits of working with early stage sci-tech companies and entrepreneurs. Peggy Garvin's article discusses the value IIPs provide to professionals working in policy, politics and journalism in navigating the information-rich and constantly evolving space of government information on the Internet. Phyllis Smith describes the value of her business to a Canadian federal government department. Arthur Weiss and Ellen Naylor offer insights into the use of secondary and primary research for competitive intelligence. Eiko Shaul discusses how an IIP fluent in the language and culture of a country (Japan, as an example) would be an asset to cross-country research. Finally, Missy Corley uses the business approaches and products of three IIPs, as well as projects her own business has undertaken, to show how IIPs in genealogical research, a specialized niche, provide value-added 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 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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.034 | 0.053 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".