Bibliographic record
Abstract
Chinese Abstract: 本文檢定企業之媒體管理與後續發生財務危機之相關性。以台灣50 成份 股為訓練樣本，使用支援向量機的演算法建立正負面新聞分類的依據。針對台 灣證券市場在1995 年至2010 年所發生的財務危機公司為觀察公司及其配對的 對照公司為研究樣本，檢定企業在爆發財務危機前是否會抑制公司負面新聞的 揭露或者進行塑造正面形象的新聞報導等媒體管理。實證結果顯示財務危機事 件日前一年，負面新聞會在各季持續揭露，然而，財務危機事件日前一季，企 業也會進行塑造正面新聞報導。English Abstract: This study examines the relationship between media management and subsequent corporate financial distress. Applying a Support Vector Machine algorithm to the component stocks on the TSEC Taiwan 50 index could establish a rule for classifying positive/negative news. By examining the behavior of these companies during periods of financial distress from 1995 to 2010 and that of control firms listed on the Taiwan Stock Exchange, we examine whether firms suppress negative news or whether they attempt to mold a positive image before exposing news of financial distress by means of media management. Our findings show that negative news would be continuously exposed in several quarters of the first year before financial distress occurs, but the positive image would be molded in the first quarter prior to the onset of financial distress.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".