Assessing the Use of Internet Surveys in the Context of Advertisement Tracking Studies: A Case Study of Tourism Yukon's Winter Promotion Campaign
Bibliographic record
Abstract
As interest in the varying applications of Internet technology has expanded in recent years, so have discussions concerning the relative merits of this medium as a credible means for conducting market survey research. Proponents of this form of on-line surveying claim that as the Internet becomes more universally accepted as a means of communication, its utility for survey purposes will be largely related to its ability to conduct some forms of research faster, better, and more conclusively than other more traditional methods of interviewing. Other supporters highlight the approach's potential research advantages with respect to gaining access to especially difficult-to-find populations, its cost effectiveness from a data collection perspective, and its speed of interaction with respondent populations. Conversely, other researchers express more cautionary perspectives and emphasize that Internet survey methods tend to suffer many of the same shortcomings as those associated with more traditional survey methods: inappropriateness for communication with specific audiences, control over sample representativeness, “self-selection” biases and response turn-around time. As with other emerging market survey research tools, there is a need to systematically explore the strengths and weaknesses of these perspectives in the context of specific research situations. This research examines issues of sample representativeness, “self selection” or non-response bias, and appropriateness of the survey techniques in the context of advertising tracking research. It does this by comparing the socio-economic and behavioral traits of Internet and traditional (telephone and mail) survey respondents participating in a tourism advertisement tracking study in Canada.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".