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
Surprises occur when organizations try to exceed the limits of their capabilities. The surprises include both serious accidents and remarkable discoveries. The idea that organizations have limits sheds light on a systemic source of organizational accidents and an important and increasingly prevalent aspect of organizational life. This article discusses various organizational limits and why they exist, it reviews factors that lead organizations to exceed their limits either intentionally or inadvertently, and it points out several reasons why limit violations may be growing more prevalent. Although some organizational limits arise from fundamental characteristics of people or technological systems, nearly all organizational limits result from rather arbitrary decisions about capacities, systems, and structures. In particular, limit violations often stem from uncertain and unintentional exploration. After examining potential consequences and symptoms of limit violation, the article proposes several reasons why researchers should add limits to their agendas for future research.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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