Investigating the level of "opportunity to see": a case of advertisements on the roadside light emitting diode video screens
Bibliographic record
Abstract
The current study investigates the level of “opportunity to see” with reference to the advertised products/service/ideas on the LED video screens in Kigali city. A total of 157 respondents were conveniently selected and interviewed just after passing by the video screens. Results demonstrate that some products/services were seen more than others and some products/services were seen in more than one video screens. It was also revealed that the there is no relationship between the time of the day, the frequency of passing by the video screen, means of transport used by the respondents and the “opportunity to see” the advertised product/service/idea seen on the video screen. However, the results indicate that there is a significant relationship between the location of the video screens, prior knowledge and the respondent having used or still using the product/service and “opportunity to see” the advertised product/service/idea. Results also suggest that LED video screen advertising can be useful for achieving the desired integrated marketing communication. The paper discusses implications, limitations and directions for future research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".