Bibliographic record
Abstract
Throughout the developing world there have been numerous studies of the impact and ethics of marketing to consumers in the lowest socio-economic demographic, known as the Bottom of the Pyramid. These consumers make less than $2 USD per day in many countries, yet will buy expensive items marketing by the media/the Internet, discussed on social media, and to keep up with peers (peer pressure). When such items are of benefit to the purchaser or their region, the result may be positive (computers for schooling, etc.) Yet spending on luxury items can cause such consumers to go deeply into debt or forego necessities, calling into question the ethics of targeting this group. In the United States, the poverty level is higher than in many countries, and access to media/social media/the Internet is ubiquitous. Americans are also susceptible to peer pressure, according to studies. Bottom of the Pyramid research, however, is lacking on American respondents. This proposal would fill that gap, considering such marketing and consumption from a behavioral and perceptive viewpoint. Hypothetical recommendations drawn from survey questions based on research questions developed through theoretical frameworks and scholarly literature review will suggest practical courses for American industry to sell to this population without ethical question. This work may also spur more in-depth analysis involving clearly defined demographic groups for deeper analysis and understanding. The research follows the qualitative method and is to be analyzed thematically using Likert format numbers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".