Evidence map›Paper›PMID 36359041›Full record

ArticleAnimals : an open access journal from MDPI2022

Portraying Animal Cruelty: A Thematic Analysis of Australian News Media Reports on Penalties for Animal Cruelty.

Rochelle Morton, Michelle L Hebart, Rachel A Ankeny, Alexandra L Whittaker

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Rochelle MortonSchool of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA 5371, Australia.ORCID 0000-0003-0328-681X
Michelle L HebartSchool of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA 5371, Australia.ORCID 0000-0002-0700-7585
Rachel A AnkenySchool of Humanities, The University of Adelaide, Adelaide, SA 5005, Australia.
Alexandra L WhittakerSchool of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA 5371, Australia.ORCID 0000-0001-9011-8296

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Media portrayals of animal cruelty can shape public understanding and perception of animal welfare law. Given that animal welfare law in Australia is guided partially by 'community expectations', the media might indirectly be influencing recent reform efforts to amend maximum penalties in Australia, through guiding and shaping public opinion. This paper reports on Australian news articles which refer to penalties for animal cruelty published between 1 June 2019 and 1 December 2019. Using the electronic database Newsbank, a total of 71 news articles were included for thematic analysis. Three contrasting themes were identified: (1) laws are not good enough; (2) laws are improving; and (3) reforms are unnecessary. We propose a penalty reform cycle to represent the relationship between themes one and two, and 'community expectations'. The cycle is as follows: media reports on recent amendments imply that 'laws are improving' (theme two). Due to a range of inherent factors in the criminal justice system, harsher sentences are not handed down by the courts, resulting in media report of 'lenient sentencing' (theme one). Hence, the public become displeased with the penal system, forming the 'community expectations', which then fuel future reform efforts. Thus, the cycle continues.

Indexed as

animal crueltyanimal lawanimal welfareFacebooknews mediapenalties

Identifiers

PMID36359041
PMCPMC9653748

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.