The Emergence and Evolution of Consumer Language Research
Bibliographic record
Abstract
Abstract Over the last 50+ years, there has been a huge rise in interest in consumer language research. This article spotlights the emergence and evolution of this area, identifying key themes and trends and highlighting topics for future research. Work has evolved from exploration of broad language concepts (e.g., rhetorics) to specific linguistic features (e.g., phonemes) and from monologues (e.g., advertiser to consumer) to two-way dialogues (e.g., consumer to service representative and back). We discuss future opportunities that arise from past trends and suggest two important shifts that prompt questions for future research: the new shift toward using voice (vs. hands) when interacting with objects and the ongoing shift toward using hands (vs. voices) to communicate with people. By synthesizing the past, and delineating a research agenda for the future, we hope to encourage more researchers to begin to explore this burgeoning area.
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".