DARK PATTERNS AND PEDAGOGY: EXPANDING SCHOLARSHIP AND CURRICULUM ON MANIPULATIVE MARKETING PRACTICES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This conference paper addresses gaps in scholarship and pedagogy surrounding the phenomenon of “dark patterns” in digital marketing and interface design by showcasing three curriculum-building projects. Dark patterns refer to a set of design strategies that co-opt the human-centred values advocated for in the fields of user experience (UX) design and human-computer interaction (HCI) to manipulate users into taking actions contrary to their personal interests. Recent dark patterns research has clustered within the fields of HCI, media studies, and game studies, with a focus on e-commerce and online gambling platforms. The presented projects put this established research into conversation with scholarship from business and marketing, science and technology studies, cognitive neuroscience, and disability studies to both create a more holistic definition of dark patterns and implement this expanded definition into university course curricula. These include $2 , focused on contextualizing dark patterns within historical market segmentation and merchandising strategies; $2 , on broadening the definition of dark patterns to include non-screen interfaces; and $2 , on analyzing how dark patterns have a disproportionate effect on individuals with certain cognitive disabilities. Collectively, these projects aimed to grant a greater historicity and social context to the phenomenon of dark patterns and introduce them as a utilizable pedagogical concept within the disciplines of communications, technology, and design. The findings of these projects are presented through the sharing of pedagogical materials, informal and formal feedback, and planned curriculum revisions.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it