Threatened by wildfires: What do firms disclose in their 10‐Ks?
Bibliographic record
Abstract
Abstract We examine the determinants of firms’ 10‐K disclosures on the threat of and exposure to wildfires. We match the location of wildfires in the United States to firms in the same county as the wildfire. We first establish that wildfire disclosure is far from widespread. On average, 6.1% of firms with wildfires in their headquarters county mention wildfire information in their 10‐Ks. Second, we find that the number of wildfire days in a company's headquarters county is a key determinant of wildfire disclosure. The sensitivity of wildfire disclosure to wildfire exposure has also increased in recent years, emanating mainly from firms having experienced wildfires impacting their past operations and in the western states and the utility and banking industries, and from those exhibiting a high level of tangible assets. Third, we find that wildfire exposure has no bearing on stock price, whereas more wildfire‐related disclosure lowers stock price.
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.
How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".