From landslide to mudslide: The strategic marketing mistakes of the 2020–2023 New Zealand Labour Government
Bibliographic record
Abstract
This article explores how a political party’s fortunes can change extremely quickly, by examining the strategic errors behind the Labour Party’s 2023 loss in New Zealand. In October 2020, Prime Minister Jacinda Ardern and the Labour Party of New Zealand won a landslide majority. This secured a once in a generation chance to deliver transformational change. However, just over 2 years later, Ardern exited following a profound drop in popularity. Although a respected minister, Chris Hipkins, took over, the party then suffered a massive defeat. We apply a playbook developed for the 2020 election to identify the reasons behind such a downturn in fortunes, noting the speed of change of voter priorities and Labour’s failure to develop a clear vision or pivot to address changed priorities. We draw on multiple sources of data, including party policies, communications, polling data and the public engagement survey Vote Compass. This confirms that governments, to maintain support, must utilise appropriate market research and engage in careful political marketing planning, starting with understanding voter expectations from the last election.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".