Evidence-Based Management: A Design Theory, Template, and Technology for a Knowledge Delivery Platform
Bibliographic record
Abstract
We take a theory-driven approach to designing an evidence management system. The ultimate purpose is to promote evidence-based management. The immediate purpose is to capture, curate, maintain, and deliver evidence gathered from both management research and management practice in a format usable by both humans and intelligent systems. The unified theory of acceptance and use of technology (UTAUT) and the theory of planned behaviour serve as the framework for the design, resulting in a design theory and empirical propositions. Our design offers a platform to facilitate ongoing communication between researchers and practitioners as well as a trustworthy mechanism for delivering the evidence to the end user in real-time. It comprises an evidence template, a wiki platform for bidirectional communication, and blockchain technology for ensuring trust among evidence users.
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 | 0.059 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".