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
SUBJECT AREAS: Agribusiness, Business processes, Canada, Competitive strategy, Corporate strategy, Insurance, Managerial economics, Managerial skills, Operations management, Quantitative analysis, Risk assessment, Risk management, Service industry CASE SETTING: Winnipeg, Canada; agriculture; $1.8 billion revenues; 1998 United Grain Growers Ltd. (UGG), a Canadian grain distributor, audited its exposure to a number of key risks, especially the impact of weather on grain volumes and operating income. Understanding these risks was crucial because the company was in the midst of a major modernization and diversification program. But though UGG already managed traditional risks through a variety of control processes, what could be done about the biggest risk - the weather? Teaching Purpose: Designed to discuss fundamental issues of risk management: Why does it make sense for a firm to manage its risks? From where do they arise? How do you measure risk?
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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