Characteristics of Commercial Leaflets Sandwiched in Newspaper (Part 5) : Distribution Volume and Trends of Commercial Leaflets by Year
Bibliographic record
Abstract
In order to analyze the distribution trends of leaflets in newspapers, the relationship between the number of leaflets and the consumer price index was examined according to year and category of business. The distribution volume of leaflets has been decreased every year. On 2017, it is estimated to have decreased by 46% compared to 2013 and will continue to decrease in the future. Distribution of large size leaflets has been decreasing every year. The leaflets that are widely distributed are A4 and A8 sizes. The major distribution period of leaflets was the second quarter (April-June), where consumption and activity are active. The amount of leaflets has been decreasing year by year. Large stores, and the business of electronics, education, and clothing showed a high consumption tendency of leaflets. The industry of food and beverage showed the slight decrease on the consumption of leaflets, and the construction, automobiles and other leaflets were distributed constantly every year. There may be various reasons on the decrease of leaflets. They include the development of information technology and other public relations media replacing the role of leaflets, the change of advertisement methods, or the business situation getting worse.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".