An evaluation framework for outcome and impact measures
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
ABSTRACT Competitive intelligence (CI) measurement practices within organizations remain fragmentary and elusive, although prescriptive CI performance and impact measures have been proposed in the literature. This study responds to calls for research into CI measurement in order to examine why organizations fail to measure CI, and to develop an evaluation framework for prescriptive measures that would support the evolution of best practices in measurement. A qualitative study (the ‘users study') consisting of interviews and shared negotiated texts with 12 users of CI was conducted. Study participants were senior managers and executives who use CI in the course of their work responsibilities at their respective individual organizations. Participants indicated that measurement cost, confused conceptualizations of CI measurement, and skepticism regarding the informativeness of measurement were obstacles to the implementation of CI measurement within their organizations. Although few participants conduct measurement activities, participants were all able to describe the ideal characteristics of CI outcome and impact measures. That list is here combined with the findings of an earlier study (the ‘experts study') conducted by the authors (Gainor & Bouthillier, ), in order to develop the evaluation framework provided here. This study provides a rare account of CI user perspectives on the rationale behind the lack of CI measurement within organizations, and a unique tool, the evaluation framework, which may be used to support both research and training within the field of CI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 it