Discursive struggles between bidding and target companies: an analysis of press releases issued during hostile takeover bids
Bibliographic record
Abstract
What are the types of interactions in the discursive struggles between the two parties involved in a hostile takeover bid? How is the legitimacy of the bid (de) constructed by both the bidding and target companies during their discursive struggles? This paper aims at addressing these research questions. Discursive struggles between the bidding and target companies are studied in a sample of 66 press releases related to seven hostile takeover bids approved by the French Market Regulator between December 2006 and December 2014. A study of the sequence followed by each party in issuing their press releases confirms the existence of strong interactions in all the hostile takeover bids studied. Using a manual content analysis methodology, we find that the disclosures made by the bidding and target companies consist of a series of attacks and defenses in which target companies are particularly offensive. We also give evidence that the two companies use legitimation, (de) legitimation and (re) legitimation arguments during discursive struggles, revealing the reciprocity of the communication between the two protagonists. We underline the symbolic or strategic dimensions of these legitimacy strategies in the view of the outcome of bids. Finally, we discuss the implications of our findings for regulators and make suggestions for future research. Based on the metaphor of ventriloquism, our research highlights the importance of considering disclosures as a dynamic and mutual influence process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".