The DOI matches its Crossref record; the references it cites are not checked yet.
Ageism in the era of digital platforms
Open accessAbstract
Ageism is the most invisible form of discrimination. While there is some awareness of gender, racial, and socioeconomic discrimination on digital platforms, ageism has received less attention. This article analyzes some tools that are frequently embedded on digital platforms from an old-age perspective, in order to increase awareness of the different ways in which ageism works. We will firstly look at how innovation teams, following homophilic patterns, disregard older people. Secondly, we will show how ageism tends to be amplified by the methods often used on digital platforms. And thirdly, we will show how corporate values contradict the usability issues that mainly affect people with a low level of (digital) skills, which is more common among older people. Counterbalancing the abusive power control of the corporations behind digital platforms and compensating for the underrepresentation of groups in less favorable situations could help to tackle such discrimination.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Researchers are thus making the case that concerns of ageism belong in the AI bias literature alongside racism, ableism, and sexism”
From Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes (2022) · cites this paper as Rosales and Fernández-Ardèvol, 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Ageist exclusion has also been identified in limited or stereotypical representation of old age or older adults themselves within data training sets”
From Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes (2022) · cites this paper as Rosales and Fernández-Ardèvol, 2020
The cited source says what the claim attributes to it.
In the citing paper
“As Rosales and Fernández-Ardèvol (2020) explain, “Ageism shapes both the image(s) that individuals have of themselves and the image(s) that society has of the different life stages.””
From Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes (2022) · cites this paper as Rosales and Fernández-Ardèvol, 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
References 81 · Cited by 137
Research works printed but not linked: 70
- AGE Platform Europe, 2016, AGE Platform Europe Position on Structural Ageism
- 10.1007/978-3-319-73820-8 · DOI
- 10.1080/08993408.2014.963363 · DOI
- 10.1017/S1041610211002936 · DOI
- 10.1145/2037373.2037383 · DOI · Paper page
- 10.1007/s41060-016-0040-z · DOI
- 10.1080/1369118X.2012.678878 · DOI · Paper page
- Brah A, 2004, Journal of International Women’s Studies, 5, 75
- 10.1016/j.ijhcs.2010.11.004 · DOI
- Buolamwini J, Gebru T (2018) Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency 81: 1–15. Available at: http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf.
- 10.7551/mitpress/4692.001.0001 · DOI · Paper page
- Cohoon JM, 2004, Women and Information Technology: Research on Underrepresentation
- 10.1177/0163443716674363 · DOI
- 10.1613/jair.4935 · DOI
- 10.2190/7XAY-PYBN-AA5L-3DRC · DOI
- 10.1145/2541016.2541040 · DOI
- Eubanks V, 2018, Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor
- Fernández-Ardèvol M, 2019, Oxford Handbook of Mobile Communication, Culture, and Information
- Fernández-Ardèvol M, Ivan L (2013) Older people and mobile communication in two European contexts. Romanian Journal of Communication and Public Relations 15(3): 83–101. Available at: http://journalofcommunication.ro/index.php/journalofcommunication/article/view/196 (accessed 1 February 2015). · DOI
- 10.1007/978-3-642-21726-5_2 · DOI
- 10.28945/3612 · DOI
- 10.2307/j.ctt9qdb6b.5 · DOI
- 10.7551/mitpress/10271.001.0001 · DOI
- 10.1016/j.socnet.2007.01.005 · DOI
- 10.1109/TKDE.2012.72 · DOI · Paper page
- 10.1145/2939672.2945386 · DOI · Paper page
- 10.1016/j.future.2018.09.024 · DOI
- 10.1145/2745197.2745210 · DOI
- Ito M, 2010, Hanging Out, Messing Around, and Geeking Out: Kids Living and Learning with New Media
- 10.22230/cjc.2017v42n2a3221 · DOI
- 10.1145/2750858.2807542 · DOI
- 10.1109/ICDMW.2012.117 · DOI
- Kaur K, Cook DM (2019) Haptic alternatives for mobile device authentication by older technology users. In: Advances in Intelligent Systems and Computing. DOI: 10.1007/978-3-319-93692-5_24. · DOI
- 10.1177/2053951714528481 · DOI · Paper page
- Kiukkonen N, 2010, Pervasive services (ICPS’10)
- Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. National Academy of Sciences 110(15): 5802–5805. Available at: http://www.pnas.org/cgi/doi/10.1073/pnas.1218772110. · DOI · Paper page
- 10.4324/9781315455334-8 · DOI
- Lee U, Lee J, Ko M, et al. (2014) Hooked on smartphones: An exploratory study on smartphone overuse among college students. In: Human factors in computing systems (CHI’14). Toronto, Canada: ACM Press, pp. 2327–2336. Available at: http://www.yatani.jp/paper/CHI2014_MobileOveruse.pdf%5Cnhttp://dl.acm.org/citation.cfm?id=2557366. · DOI
- 10.1080/1369118X.2012.676056 · DOI
- 10.5749/minnesota/9780816677948.003.0047 · DOI
- 10.1146/annurev.soc.27.1.415 · DOI · Paper page
- Neff G, 2016, International Journal of Communication, 10, 4915
- 10.15353/joci.v8i1.3061 · DOI
- O’Neil C, 2016, Weapons of Math Destruction. How Big Data Increases Inequality and Threatens Democracy
- 10.1145/2145204.2145228 · DOI
- Palmore EB, 1999, Ageism: Negative and Positive
- 10.4159/harvard.9780674736061 · DOI · Paper page
- 10.1137/1.9781611972795.50 · DOI
- 10.1145/2740908.2742765 · DOI
- 10.4324/9781315455334-3 · DOI
- 10.1145/2371574.2371577 · DOI
- 10.1007/978-3-030-06076-3_4 · DOI
- Sarbaz S, 2016, Annals of Oncology, 27, ix189, 10.1016/S0923-7534(21)00742-0 · DOI
- Sawchuk K, Crow B (2011) Into the grey zone: Seniors, cell phones and milieus that matter. WI: Journal of Mobile Media 5(1). Available at: http://wi.mobilities.ca/into-the-grey-zone-seniors-cell-phones-and-milieus-that-matter/ (accessed 6 September 2011).
- 10.1515/9789048531011 · DOI
- 10.1145/2370216.2370243 · DOI
- 10.1177/0003122414531776 · DOI
- 10.1109/ICCSP.2016.7754189 · DOI
- 10.1145/2632048.2632052 · DOI
- Stangor C, Schaller M (2000) Stereotypes as individual and collective representations. In: Stangor C (ed) Stereotypes and prejudice: Essential readings. Philadelphia, PA: Psychology Press, pp. 64–82. Available at: https://books.google.es/books?id=3yJm1h1OblEC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false.
- 10.31211/interacoes.n34.2018.a2 · DOI
- 10.1016/j.poetic.2006.05.004 · DOI · Paper page
- 10.1093/oso/9780190889760.001.0001 · DOI · Paper page
- Wachter-Boettcher S, 2018, Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
- Wagner DT, 2013, Proceedings of the international conference on mobile and ubiquitous systems, 1
- 10.1177/1461444815571112 · DOI
- 10.1016/j.chb.2016.04.011 · DOI
- 10.1145/1455770.1455839 · DOI
- 10.1145/2307636.2307648 · DOI
- 10.1073/pnas.1418680112 · DOI · Paper page
Metadata
- Authors
- Rosales, A., & Fernández-Ardèvol, M.
- Affiliations
- Universitat Oberta de Catalunya (ES)
- Corresponding author
- Rosales, Andrea
- Venue
- Convergence: The International Journal of Research into New Media Technologies
- Publisher
- SAGE Publications
- Published
- 2020-06-25
- Volume
- 26
- Issue
- 5-6
- Pages
- 1074-1087
- ISSN
- 1354-8565 (ISSN-L)
- 1748-7382
- PMC
- PMC7649948
- OpenAlex
- W3037263482
- Cited by
- 137
- References
- 81
- Citations by year
- 2026: 25 · 2025: 26 · 2024: 16 · 2023: 31 · 2022: 27 · 2021: 12
- FWCI
- 26.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Convergence The International Journal of Research into New Media Technologies (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- The Oberta in Open Access (Universitat Oberta de Catalunya) (submittedVersion, cc-by-nc)
- Funders
- The Spanish Ministry of Science, Innovation, and Universities (FJCI-2015-24120)
- Social Sciences and Humanities Research Council of Canada (895-2013-1018)
- Topics
- Health Sciences › Medicine › Geriatrics and Gerontology › Technology Use by Older Adults
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- ageism, digital platforms, older adults, age discrimination, homophily, innovation teams, usability, digital skills, organizational values, power control, underrepresentation
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Ageism in the era of digital platforms. https://app.cochat.ai/papers/10.1177/1354856520930905
- The paper
- Rosales, A., & Fernández-Ardèvol, M. (2020). Ageism in the era of digital platforms. Convergence: The International Journal of Research into New Media Technologies. https://doi.org/10.1177/1354856520930905
