Understanding Variation in Audience Engagement and Response: An Application of the Composite Model to Receptions of<i>Avatar</i>(2009)
Bibliographic record
Abstract
Although much research documents variations in viewers' responses to screen media, the basis for divergent receptions remains relatively poorly understood and inadequately conceptualized. One possible theoretical schema is offered in the composite multidimensional model, which charts 4 distinct modes of reception that shape the specific form and content of audience responses in different contexts. In this study, the core distinctions charted in the composite model were tested in a Q methodology study of cross-cultural receptions of Avatar (2009). 120 respondents from 27 countries modeled their subjective responses to this polysemic text by rank-ordering 32 items and then commenting on their selections. Through factor analysis, 4 discrete responses toward Avatar were identified among participants, accounting for 74% of all respondents. Each factor clearly reflects key elements of the transparent, referential, mediated, and discursive modes identified in the composite model, indicating that the model is reasonably accurate in identifying broad distinctions in the underlying approaches to meaning-making that can be adopted by different viewers. Suggestive associations between viewers' subjective orientations and demographic characteristics, social group memberships, and discursive affiliations were also documented.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".