On the “Realities” of Investor‐Manager Interactivity: Baudrillard, Hyperreality, and Management Q&A Sessions
Bibliographic record
Abstract
ABSTRACT This paper draws on extensive fieldwork to explore the nature, scope, and implications of management preparations for the question and answer session (Q&A) that occurs during a firm's results presentation. Prior literature has associated the value of this encounter with investor‐manager interactivity. As such, it is assumed that there are high levels of managerial authorship, ownership, and spontaneity as executives face questions from analysts. Following on, it is generally assumed that this translates into an increased risk of unintended disclosure, through verbal and nonverbal messaging. However, our data indicate that management engages in vast preparatory work to mitigate the risks associated with real (“original,” natural, spontaneous, un‐staged) interactivity. Instead, the Q&A is carefully planned, organized, scripted, and rehearsed. As such, the event is transformed into a hyperreal encounter in the Baudrillardian sense. Despite this, the value of the Q&A is not necessarily impaired. Instead, these managerial backstage preparations arguably make the encounter realer than real. We suggest that the Q&A that we observe (the “copy”) is more useful than the original might have been. Our work provides evidence and discussion of two interconnected paradoxes: perfection and self‐reference. This study not only raises important questions, challenges, and opportunities for researchers interested in the study of investor‐manager interactions but also speaks to those with an interest in workplace meetings and Q&A more broadly.
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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 | 0.029 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".