Vote Compass in the 2014 New Zealand election
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vote Compass – an online voter education tool originating in Canada – was used for the first time in New Zealand during the 2014 general election. During its inaugural run, over 330,000 New Zealanders visited the Vote Compass website to answer 30 policy- or issue-based questions. In return, respondents received a report on how close their views were to 10 political parties seeking office. Due to the large sample size, these data provided Television New Zealand with unique insights into voters’ views that could also be related to party policies and campaign events by academic commentators. After explaining the nature of the tool and describing the composition of the New Zealand-based team, this article examines the implications that Vote Compass has for party responsiveness and political marketing. In particular, we note the importance of Vote Compass not just for market-oriented policy, but for the overall leadership brand, including its ability to deliver on promised goods. The article also reflects on the contribution that the tool makes to voter engagement and democracy in general. Lastly, it provides a summary of the overall Vote Compass data from the main survey items and marketing-related post-election survey data in an appendix for academics to use in their own research and teaching in future.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it