Understanding the social structure of television audiences: three essays
Bibliographic record
Abstract
Essay 1: “Measuring group viewing in television audiences”. Group viewing (GV) has been shown to increase enjoyment of television programs, to affect viewers’ intention to watch and to mediate cognitive processing of advertisements. We propose a simple measure of GV that can be easily incorporated by ratings measurement companies, and apply it to a unique Mexican people meter data set to show how demographic, psychographic and program content characteristics affect the level of GV. Furthermore, we show that consequences of GV include increasing individual viewership and reduced channel switching, and that these effects interact with group composition. Essay 2: “Group viewing and spousal preferences as predictors of individual preferences of Mexican television viewers”. We investigate the impact of GV on individual television consumption while accounting for other mutual influences between the members of the specific group consisting of husband and wife. Our findings indicate that most mutual influences between wives and husbands stem from GV. The residual influences are small although interesting: husbands’ influence on wives’ preferences becomes negative once group viewing is accounted for, but wives’ influence on husbands’ remains positive. We discuss consistency of these findings with past research and their implications for marketers and broadcasters. Essay 3: “Television auditoria: Hierarchical networks meet consumption systems”. Most empirical research on television audiences has framed audiences as demand sides of markets made up by unrelated individuals, while scholarship in communication has not delivered a unified theory of audiences. We present an empirically-grounded framework from a systems perspective, where television audiences and channels form broader systems we call auditoria. The sub-system audience is specified as a hierarchical network while the subsystem source (channels) is specified as a consumption system, building on extant marketing theory. We identify emerging properties of auditoria which explain consistent findings in the literature and allow us to formulate theoretical propositions. Findings from essays 1 and 2 are integrated into the proposed framework.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".