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
Using data from 38,916 businesses in war-torn Colombia and from 5,138 attacks by the two rebel groups, FARC and ELN, we study how firms manage inventory during civil war. We obtain exogenous variation in the conflict intensity via a difference-in-differences model, which hinges on the peace process between Colombia’s government and FARC. Relying on this identification strategy, we hypothesize and show that war causes two effects on firm-level inventories. First, it leads firms to replace physical assets (inventory) with fungible assets (cash), causing them to operate with an oversecured financial buffer, but a fragile operational buffer. Second, this inventory reduction occurs mostly in unprocessed inventories (finished-goods inventories are insensitive to violence), meaning that, although war-torn businesses are equipped to fulfill planned orders, they become inflexible at handling uncertain future demand. We then show that the magnitude of these effects is highly contingent on the firm’s position in the supply chain, its proximity to distribution markets, and the type of attacks it is subject to. We then propose policies to address war-related risk in supply chains. This paper was accepted by Vishal Gaur, operations management.
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 | 0.000 |
| 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 | 0.000 |
| 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