Bibliographic record
Abstract
In this paper we will be analyzing how to advertise and market a tool that uses augmented reality to teach the basics of videography to students. Our product is aimed at people that don’t have the accessibility or the financial resources to get a video camera but want to learn. Our market is 12- 17 year-old users, we will be trying to market to schools directly for access to this demographic. We have chosen this demographic to correlate our product to a real problem. This problem is the exclusivity of Videography, the cost of a standard camera is proven to be too expensive for people that want to learn at a young age. Since there is a higher demand for videography skills in the workplace, we see this as an economic solution for those who want to learn the basics and see if they like it enough to invest in this artform. How do we get this tool into as many classrooms as possible? Since there are no products like this in schools, we will base our assumptions on research into how new technologies are selected in schools or in households. We will explore three different approaches: advertise to the kids themselves, advertising to the parents but our analysis concludes that the most effective way to solve this problem is to advertise this product to school associations and school boards.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.023 |
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".