Perceptions of ageist acts and those who confront them: implications for intersectional older targets
Bibliographic record
Abstract
With the changing face of the Canadian population, older adults now make up a larger proportion of the total population than ever before (Statistics Canada, 2021). Regardless of this trend and the fact that ageism is prevalent and growing over time in Canada, (WHO, 2021; Heritage, 2020; Godley, 2018; Allore et al., 2015) ageism remains largely understudied. In particular, there has been little research on ageism that has been conducted with an intersectional perspective. This is an area that requires further attention, as previous research has demonstrated the important role of intersectional identities in shaping person perception (Neuberg & Sng, 2013; Ghavami & Peplau 2013). For instance, Neuberg et al., (2013) revealed that a target’s age, sex, and home ecology (characterized as “desperation” versus “hopeful”) intersect to shape how they are perceived by others, and Ghavami et al., (2013) demonstrated how a target’s race and gender may intersect to form unique perceptions about them. While age has often been overlooked in intersectionality studies, Kang et al. (2014) provided a preliminary understanding of how age interacts with race to influence how male targets are perceived. Their findings demonstrate the importance of examining how ageism is manifested towards older adults with different intersecting race and gender identities. In addition, their findings suggest that older targets may face differential consequences when engaging in prejudice reduction strategies, such as when confronting a perpetrator. Therefore, in this proposal, I will take a social psychological perspective to understand how ageist actions are perceived when targeting older adults with different intersecting identities, and if these identities also result in differential consequences for older confronters of ageism.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".