Hall’s encoding/decoding model revisited in the digital platform age: de/encoding, lincoding, affordecoding, and en/decoding
Bibliographic record
Abstract
This paper updates Stuart Hall’s encoding/decoding model to examine the reproduction and contestation of dominant-hegemonic ideologies in the digital platform era. While Hall’s model analyzed television broadcasting within mass media systems, today’s communication processes are fundamentally transformed by the rise of platform capitalism. A key contribution involves replacing Hall’s ‘relations of production’ with ‘social positions’ to address intersecting systems of inequalities in a media environment where the boundaries between message producers and consumers have become blurred. Building on this foundation, this paper introduces four interconnected concepts: de/encoding (media/content producers’ creation of messages based on extracted user data), lincoding (connection of users with messages, platforms themselves, and other users through algorithmic systems exercised by AI and platform workers), affordecoding (users’ interpretation and utilization of platform affordances), and en/decoding (users’ dual roles as message consumers and producers). The resulting DLAE (De/encoding, Lincoding, Affordecoding, and En/decoding) model provides a tentative theoretical framework for understanding how multiple dominant-hegemonic ideologies are maintained and challenged through digital communication processes. While acknowledging the intensifying reproduction of dominant-hegemonic ideologies through commercial platforms, the model simultaneously recognizes possibilities for user resistance and negotiation through interactive media technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".