Evaluating the Impact and Value of Competitive Intelligence From The users Perspective - The Case of the National Research Council’s Technical Intelligence Unit
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
Understanding and being able to measure and prove the impact and value of intelligence is of significant importance. The objective of this study was to develop an evaluation instrument that the users of intelligence could fill in that could be used to assess both the impact and value of the intelligence they received. Starting with an evaluation instrument based on lists of benefits identified in the competitive intelligence literature, measures of these benefits and client satisfaction/service quality metrics, the study researchers interviewed clients of one large government competitive technical intelligence organization asking them to articulate the benefits they obtained from the intelligence they received and methods for evaluating these benefits. All users of intelligence identified benefits they had received from the intelligence received. Additional benefits beyond those that are in the current literature were identified by those interviewed. In terms of measurement of these benefits, intelligence users (the clients) understood why hard financial type measures for example ROI or dollar impact on performance was important (especially in their organization) they felt that assessing these for the intelligence they received would be difficult but that softer, more subjective measurement such as extent to which the user agrees that the intelligence provided the intended benefit could be used. Additional perceptual based indicators of service quality and customer satisfaction measures were also suggested by intelligence clients. Based onthe results of the literature review and interviews, an intelligence evaluation instrument was developed that asks the clients to assess the extent to which they have realized one or more of 27 impacts identified in this study as well as assessing 10 elements of service quality.
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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it