Bibliographic record
Abstract
A major concern among advertisers and media managers is the measurement of net audience coverage achieved by an advertising campaign over time and across combinations of publications. Measures of audience exposure for combinations of publications have been shown to be more accurate when based on audience segments associated with each publication than when based on aggregate exposure to all the publications in the group. This thesis argues that the concept of duplication among audience segments associated with a combination of individual publications is equally applicable to the segments associated with the sections of a single publication. Accordingly, it is the objective of this thesis to demonstrate that audience measures based on audience segments associated with sections of a publication are superior to those measures based on aggregate exposure to that publication. The fundamental measures of audience exposure are un-duplicated audience or net reach, duplicated audience and average frequency of exposure. The relationships among these measures were developed in a theoretical model of intersection duplication. The model was then applied to data drawn from a recent study on a major Canadian newspaper. As any application of the segmented audience concept depends on a simple and accurate method of estimating net reach for a combination of sections, considerable effort was expended to describe recent research concerning estimation of net reach for combinations of publications and to relate such research to the objectives of this thesis. It was concluded that segmented audience data are superior to aggregate data as a basis for audience measurement, and therefore, an advertiser must evaluate, according to advertising objectives, the placement of his advertisements and the inherent trade-off between net reach and frequency for a given advertising campaign. The paper closes with some suggestions for further study.
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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".