Actively encouraging online responses to a mixed-mode mail and web survey: a case of nudging anglers in Ontario, Canada
Bibliographic record
Abstract
Little effort has focused on encouraging less costly, online responses within mixed-mode surveys. From an Ontario angler survey, this gap is addressed by applying a nudge (i.e., mail push to web) or control (i.e., providing a print questionnaire and return envelope) for the final mail contact. The nudge increased the share of online responses from the final contact by over 40% while only slightly decreasing (1.3%) the response rate. The nudge most heavily affected senior anglers (65–70 years old) who experienced a 58% increase in online responses but an 11% decrease to the response rate from the final contact. The increased response rate for the control came at a cost of about $25 and $4 (CAD 2021) per additional response for overall and senior anglers, respectively. Thus, the cost-effectiveness of the nudge depends on the population being targeted.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".