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
As modern organizations increasingly need to operate in uncertain and fast-paced business environments, pressures increase on information systems (IS) to support these enterprises in a dynamically changing world. Consequently, systems need to deliver results given incompletely known and constantly changing requirements and contexts and other uncertainties. Their development is no longer a progression from clear and stable requirements to solutions meeting them. Rather, it is a continuous process involving multiple iterations of analysis and exploration, design, and development taking into consideration changing organizational needs, available resources, and feedback from previous iterations. Since current modeling and analysis notations generally assume stable and predictable settings for IS development, this paper explores the difficulties in applying several such techniques for modeling continuously evolving systems in uncertain and rapidly changing socio-technical domains and identifies requirements for a comprehensive modeling notation suitable for these environments. Business intelligence capability implementation in enterprises is used as an illustration.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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