Evidence map›Paper›PMID 34548739›Full record

ArticleWorld development2021

Mobilising urban knowledge in an infodemic: Urban observatories, sustainable development and the COVID-19 crisis.

Michele Acuto, Ariana Dickey, Stephanie Butcher, Carla-Leanne Washbourne

Abstract read
In one paragraph

Article in World development, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

4 authors.

Michele AcutoUniversity of Melbourne, Australia.
Ariana DickeyUniversity of Melbourne, Australia.
Stephanie ButcherUniversity of Melbourne, Australia.
Carla-Leanne WashbourneUniversity College London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Along with disastrous health and economic implications, COVID-19 has also been an epidemic of misinformation and rumours - an 'infodemic'. The desire for robust, evidence-based policymaking in this time of disruption has been at the heart of the multilateral response to the crisis, not least in terms of supporting a continuing agenda for global sustainable development. The role of boundary-spanning knowledge institutions in this context could be pivotal, not least in cities, where much of the pandemic has struck. 'Urban observatories' have emerged as an example of such institutions; harbouring great potential to produce and share knowledge supporting sustainable and equitable processes of recovery. Building on four 'live' case studies during the crisis of institutions based in Johannesburg, Karachi, Freetown and Bangalore, our research note aims to capture the role of these institutions, and what it means to span knowledge boundaries in the current crisis. We do so with an eye towards a better understanding of their knowledge mobilisation practices in contributing towards sustainable urban development. We highlight that the crisis offers a key window for urban observatories to play a progressive and effective role for sustainable and inclusive development. However, we also underline continuing challenges in these boundary knowledge dynamics: including issues of institutional trust, inequality of voices, collective memory, and the balance between normative and advisory roles for observatories.

Indexed as

Boundary-spanningInfodemicKnowledge translationScience-policy interfaceUrban observatories

Identifiers

PMID34548739
PMCPMC8446705

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.