Designing user engagement for cognitively-enhanced processes
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
A natural way to ease the introduction of cognitive computing capabilities into a user organization is through already well-established applications such as business process management (BPM) systems. Cognitive capabilities can enhance a business process by offering analytics-based recommendations on decisions and increasingly sophisticated automation through machine learning. Yet the organizational adoption of such advanced capabilities is not straightforward. Unlike conventional IT systems whose functionalities and correct operation are more transparent, user acceptance of advice and recommendations from an automated system requires development of trust over time. Additional supporting processes may emerge and evolve over a period of time to monitor, evaluate, adjust, or modify the cognitively-enhanced business process so as to enable personnel to adapt to the enhanced capabilities. In this paper, we propose that a systematic model-based approach can ease the transition to cognitive business operations. The use of suitable modeling techniques can facilitate the uncovering and analysis of obstacles to adoption, and guide the systematic search for viable modes of interaction and cooperation between human user and cognitive advisor.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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