Linguistic Information Quality in Customers' Forward‐Looking Disclosures and Suppliers' Investment Decisions
Bibliographic record
Abstract
ABSTRACT This study examines whether and how linguistic information quality (measured by readability) of customer firms' management earnings forecast reports (MEFRs) affects supplier firms' investment quality (measured by investment efficiency). Our analyses reveal that supplier investment efficiency is positively associated with the average linguistic information quality of customers' prior MEFRs, and the positive association between supplier investment efficiency and customer MEFRs' numerical information quality is stronger in supplier firms with more readable customer MEFRs. Our analyses also reveal that higher linguistic information quality of customer MEFRs improves the monitoring of supplier firms by their outside stakeholders, such as institutional investors and financial analysts, and ameliorates the negative impact of suppliers' customer‐dependence on their investment efficiency. Our results suggest that greater linguistic information quality of a customer firm's forward‐looking disclosures is associated with higher‐quality investments made by its suppliers along the supply chain.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".