Whether or not to use a quick response code in the ad
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
QR codes have a multitude of benefits for both the scanning consumers and advertisers. We empirically examine print ads in Fortune magazine to explore the factors behind a company's decision on whether or not to use QR codes in its print ads. In our model, we focus on the role of a company's past decisions as well as its competitors' past decisions. We adopt a binary logit model with multiple explanatory variables to control for advertiser type, past behaviour, and past competitive behaviour. We find that companies are likely to be influenced by their own past behaviour in their decision to use QR codes in their print ads. We also find that companies are more likely to start adopting QR codes when their competitors have done so in the past. To the best of our knowledge, this is the first attempt to examine QR codes in a descriptive, objective, multivariate, scientific study. Although the incidence of QR codes is currently low, we expect increased overall usage of QR codes in the future because of strong inertia and mimicry effects we find in our empirical investigation.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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