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Record W4413358322 · doi:10.36922/ijps025130051

Ageism in the workplace from Singapore to Canada: A translational perspective

2025· article· en· W4413358322 on OpenAlexaboutno aff
Laura Ng, Swapna Dayanandan

Bibliographic record

VenueInternational Journal of Population Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Translational researchSociologyPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

This perspective piece tackles transnational ageism in the workplace by focusing on the Singapore case and national context while making connections to timely observations in Canada. Following existing studies, our review finds that although cultural circumstances of Confucian filial piety (in Singapore and East Asia) lead to higher degrees of implicit rather than explicit age discrimination, ageism is equally serious, and similarly intense, across contexts. Our discussion challenges the oversimplification of cultural differences between “East” and “West” in how societies are thought to address aging and age discrimination, as well as how ageism in various settings of daily life manifests. By analyzing policies, workplace practices, and social attitudes in Singapore, then situating these in global trends such as in Canada, we reveal common anxieties faced by older adults regarding financial insecurity and access to re-employment. This transnational lens underscores the importance of delving deeper into the culturally specific ways ageism manifests while simultaneously working toward the creation of effective international strategies. Deeper shifts are needed in the hearts and minds of people for significant changes to occur. In our view, shifting global demographics and rapid workplace changes necessitate a move beyond stereotypes and toward intergenerational cooperation, especially but not just in the workplace. We emphasize the importance of addressing ageism at all levels, relationally and transnationally, interpersonally to institutionally, to promote age-inclusive societies and secure a more dignified future for aging populations. Both authors’ perspectives are anchored in lived experiences as Singapore citizens. In what follows, we weave together our academic and community-engaged practitioner expertise in Singapore’s context of workplace ageism in light of ongoing community and social gerontology trends. We offer some observations in Canada for a comparative lens by way of gesturing to future transnational research directions for population studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.205
GPT teacher head0.496
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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