Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 20

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

How algorithmic cultural recommendation influence the marketing of cultural collections

Wilson-Barnao, Caroline·2017·Consumption Markets & Culture

Abstract

Museums make their collections available online to keep pace with developments in how people access and share information. While museums have traditionally understood the notion of public access as part of their institutional remit, in this paper I draw on policy documents and qualitative interviews with Australasian cultural professionals, to examine how the discourse of access might account for the museum’s transformation from a community space to a resource that is beneficial to marketers. I use Google Arts & Culture as a case study, to suggest the terms of public access have altered to adapt to the needs of commercial “digital enclosures.” When people engage with the museum in virtual spaces data are collected. Algorithms work as a set of instructions that make it possible to search, sort and organise the data, linking together people and their online practices in order to enact a form of algorithmic cultural recommendation.

Overview

Integrity signals

Registry

Crossref · 10.1080/10253866.2017.1331910

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 10

  1. Andrejevic Mark., 2014, International Journal of Communication, 8, 1673
  2. Couldry Nick, 2014, International Journal of Communication, 8, 1710
  3. 10.7551/mitpress/9780262525374.003.0009 · DOI · Paper page
  4. Gray Richard., 2015, Daily Mail Australia
  5. 10.1080/01972240050133634 · DOI
  6. Kent Allen, 2000, Encyclopaedia of Library and Information Science
  7. Manovich Lev., 2010, Museums in a Digital Age, 64
  8. Pearce Susan., 1986, Museums Journal, 85, 198
  9. Pulh Mathilde, 2015, International Journal of Arts Management, 18, 43
  10. Rogers Richard., 2009, Deep Search: The Politics of Search Engines, 12

Metadata

Authors
Wilson-Barnao, C.
Affiliations
School of Communication and Arts, University of Queensland, St Lucia, Australia
Venue
Consumption Markets & Culture
Published
2017-06-13
Volume
20
Issue
6
Pages
559-574
ISSN
  • 1025-3866
  • 1477-223X
Cited by
20
References
47
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.