Evidence map›Paper›PMID 31910777›Full record

ArticleJournal of the Royal Society, Interface2020

Kia kaua te reo e rite ki te moa, ka ngaro: do not let the language suffer the same fate as the moa.

Tessa Barrett-Walker, Michael J Plank, Rachael Ka'ai-Mahuta, Daniel Hikuroa, Alex James

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tessa Barrett-WalkerAotahi - School of Māori and Indigenous Studies, University of Canterbury, Christchurch, New Zealand.
Michael J PlankSchool of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
Rachael Ka'ai-MahutaTe Pūnaha Matatini, New Zealand Centre of Research Excellence, Auckland, New Zealand.
Daniel HikuroaTe Pūnaha Matatini, New Zealand Centre of Research Excellence, Auckland, New Zealand.
Alex JamesSchool of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

More than a third of the world's languages are currently classified as endangered and more than half are expected to go extinct by 2100. Strategies aimed at revitalizing endangered languages have been implemented in numerous countries, with varying degrees of success. Here, we develop a new model regarding language transmission by dividing the population into defined proficiency categories and dynamically quantifying transition rates between categories. The model can predict changes in proficiency levels over time and, ultimately, whether a given endangered language is on a long-term trajectory towards extinction or recovery. We calibrate the model using data from Wales and show that the model predicts that the Welsh language will thrive in the long term. We then apply the model to te reo Māori, the indigenous language of New Zealand, as a case study. Initial conditions for this model are estimated using New Zealand census data. We modify the model to describe a country, such as New Zealand, where the endangered language is associated with a particular subpopulation representing the indigenous people. We conclude that, with current learning rates, te reo Māori is on a pathway towards extinction, but identify strategies that could help restore it to an upward trajectory.

Indexed as

LanguageHumansNew ZealandWalesdynamical modelendangered languageindigenous languagelanguage learninglanguage revitalizationlanguage transmission

Identifiers

PMID31910777
PMCPMC7014795

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.