A Reply to “A Comment on ‘Is Having More Channels Really Better? A Model of Competition Among Commercial Television Broadcasters’ ”
Bibliographic record
Abstract
Liu et al. [Liu, Y., D. S. Putler, C. B. Weinberg. 2004. Is having more channels really better? A model of competition among commercial television broadcasters. Marketing Sci. 23(1) 120–133] examine the television broadcast industry using a model in which profit-maximizing broadcasters seek to gain viewers by choosing the type of program to offer and by spending money to set program quality, allowing broadcasters to sell access to those viewers (through inserted advertisements) at a fixed rate per viewer. Wu and Chou [Wu, C., S. Chou. 2006. Commentary on “Is having more channels really better? A model of competition among commercial television broadcasters”. Marketing Sci. 25(5) 541–545] argue that the duopoly result for a certain range of the cost parameter in Liu et al. is not a pure strategy Nash equilibrium. They further propose some modifications to the original model to restore Liu et al.’s results. In this reply, we demonstrate how a single strategy, not included in the strategy space of the Liu et al. duopoly model leads to the difference between our analysis and that of Wu and Chou. While we had intended to rule out this strategy, the text was not entirely clear on this issue; Wu and Chou’s comment provides an opportunity to clarify the situation. We provide both empirical and theoretical support for excluding this strategy, which allows us to focus on the more plausible competitive situations in television broadcasting. We also reply to Wu and Chou’s other comments on several issues, such as the relative importance of program type versus quality.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.063 | 0.049 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".