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Record W4249287015 · doi:10.1108/oxan-db213731

Loose New Zealand monetary policy may overheat housing

2016· other· en· W4249287015 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2016
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicNew Zealand Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaImmigrationPrime ministerInflation (cosmology)EconomicsReal gross domestic productMonetary policyQuarter (Canadian coin)CashCapital (architecture)Interest rateEconomyPoliticsMonetary economicsGeographyFinancePolitical scienceDemography

Abstract

fetched live from OpenAlex

Significance New Zealand registered 3.6% year-on-year real GDP growth in the second quarter of 2016, one of the highest among developed economies. This reflects strong domestic and trading performance, particularly in construction and inbound tourism. However, record net immigration stimulated growth and per capita GDP growth was only 0.7%. Inflation remains close to zero, challenging the RBNZ, which in August cut its official cash rate by 25 basis points to record lows, with further monetary easing expected this year. Impacts A change of government in the election to be held before November 18, 2017 could change New Zealand's fiscal outlook. US failure to ratify the Trans-Pacific Partnership this year would mean the loss of much political capital by Prime Minister John Key. The 40.5% increase in milk powder prices from July to the highest levels for 2016 will provide a much-needed boon to dairy farmers.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1200.020

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.

Opus teacher head0.023
GPT teacher head0.247
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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