Using Earnings Calls to Understand the Political Behavior of Major Polluters
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
Abstract The role that private actors play in accelerating or preventing progressive climate policy and true decarbonization is a core research interest of global environmental politics. Yet scholars have struggled to measure the political behavior of multinational firms due to lack of transparency about their activities and inconsistency in reporting requirements across jurisdictions. In this research note, we present a new data source—firms’ earnings calls—that scholars might use to better understand the political behavior of major multinational polluters. To illustrate the value of earnings calls as a data source, we construct an original data set of all earnings calls made between 2005 and 2019 by major oil and gas firms. We then code these transcripts, demonstrating that although firms can be classified as more or less pro-climate, there is little evidence of the industry’s public acceptance of decarbonization. These unique data could permit researchers to explore important questions about climate politics, the evolution of private governance, and the relationship between policy and firms’ political behavior. Moreover, we suggest extensions of our approach, including other multinational industries that are amenable to this type of analysis.
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 | 0.000 |
| 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