The emergence of Temporal Reasoning Capabilities in Corporate Sustainability Reporting
Bibliographic record
Abstract
This study abductively elaborates the relationship between cognitive linguistics and attention structures in the aftermath of the financial crisis. Using latent dirichlet allocation (LDA) topic modeling for 1,581 sustainability reports issued by 339 public firms in the U.S. and Canada from 2009 to 2017, we show that subtle linguistic updates in word assemblages capturing firms’ financial and/or social responsibilities combine with proximal versus distant chronotypes (spatio-temporal frames). Fuzzy set qualitative comparative analysis for a subset of 86 frequent reporters reveals the emergence of latent classes robustly associated with three dictionary-based measures of long-termism (time horizons, long-term orientation, and future focus) collected year later. Long-termism requires convergence of distal chronotypes and social responsibilities. Firms resist long-termism through divergence between distal chronotypes on one hand and social and financial responsibilities on the other. Our study is among the first to explain why firms may develop opposite temporal reasoning capabilities while adapting to the same crisis.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".