Library Trends 43 (2) 1994: The Library in Corporate Intelligence Activities
Bibliographic record
Abstract
Economic environment Potential competitionSocial and community environment Growth opportunities Demographics Technological environment Suppliers Markets Acquisition candidates Political and regulatory environmentMost of the current literature emphasizes that BI and related terms, such as CI, refer to lega2 information gathering, not to unethical or illegal practices.Gilad and Gilad state outright that, "[b]usiness intelligence is not industrial espionage.The latter is an overrated and largely ineffective-not to mention illegal-way of gaining temporary access to the golden egg, used by those who lack the skill to raise the right goose" (p.viii).Sutton (1988) accepts that "competitor intelligence" and "competitive intelligence" are synonymous.He further distinguishes competitive intelligence from basic market-share and producttracking information by the strategic purpose of the former (p-4).A clear definition is provided by Vella & McGonagle (1988): "Competitive intelligence is the use of public sources to develop information on competition, competitors, and the market environment" (p. 1).An enumeration of the phases of CI is informative:Other major categories, each with its own list of elements, include the following: sales; pricing policies; sales force and customers, marketing, personnel, resources, and facilities; technology, research, How IS COMPETITIVE APPLIED? INTELLIGENCEUses of data acquired by competitive intelligence professionals are as varied as the activities of organizations in general.In the broadest terms, CI is information that supports positive change in an organization.More specifically, a Conference Board, Inc. survey reported the following examples of decisions relying o n monitoring information (Sutton, 1988): pricing, strategy, new products/services, acquisitions, changes in manufacturing capacity/processes, product specifications, sales force changes, advertising/promotion, and joint ventures (p.20).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.127 | 0.049 |
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".