Library Trends 43 (2) 1994: The Library in Corporate Intelligence Activities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
COMPANIES OF EVERY SIZE have established formal or informal methods of learning about their industries and competitors.While many organizations do rely on their own libraries or information centers for some of this kind of information, there continues to be a trend toward the establishment of "intelligence departments," sometimes called "BI" (business intelligence), "CI" (competitive, competitor, or corporate intelligence), or "ICN" (internal collection network) departments.The literature on this topic is commonly found in business and management journals rather than library journals and frequently addresses topics already very familiar to business librarians-i.e., finding information on products, industries, companies, and the business environment.Not all kinds of information gathering are considered to be appropriate for corporate libraries.Extra-library methods can involve field work, informal or formal, during which information is acquired from other than printed or electronic sources-e.g., sales visits, trade shows, consultants, advertising agencies, commercial credit agencies, and competitor intelligence firms.Stressed by the leading authors in the field is that these legal activities must be clearly distinguished from the illegal and unethical techniques of corporate spying or industrial espionage.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it