Interpretability and social power, or, why postmodern advertising works
Bibliographic record
Abstract
It is commonplace to recognize in contemporary advertising a ‘hyperreality’ associated with the greatly expanded and intensified use of images and simulations. While the predominance of an image culture seems to de-emphasize the cognitive generation of meaning that is achieved through linguistically mediated logic, consumers are nevertheless expected to respond with far greater ‘interpretive reflexivity’ to such advertising, a response that activates their cognitive engagement. This article seeks to explain why such ‘postmodern’ advertising works with reference to the political economy of postmodern capitalism and by developing the concept of ‘interpretive power’ that is drawn from Jürgen Habermass’s theory of communicative action and his notion of intelligibility. I argue that postmodern media culture increasingly relies upon an orientation toward validity that the ‘commodity aesthetics’ of earlier advertising either minimized or did not require. Accompanying this demonstration is a contextualization and analysis of an emblematic case of postmodern advertising. I conclude that, far from necessarily signaling a profound crisis in meaning as some commentators assert, a postmodern media culture that relies more and more on the interpretive ‘communicative competence’ of its addressees suggests both greater potential power for cultural commodification as well as greater potential resistance to this by consumers.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.097 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".