Bibliographic record
Abstract
The term ageism, coined by Robert N. Butler (1969), refers to the stereotyping of and prejudice against individuals or groups based on their age.According to Todd Nelson (2005), there has been little study and research on ageism as a form of prejudice, compared to racism and gender bias.As the baby boom generation (those born in Canada and the U.S. between 1946 and 1966) starts to consider retirement, it is about to come face to face with ageism on a scale never seen before.Within the next five years, North America is about to go from an unusually low number of people entering yearly into the "normal" retirement age of 65 to the highest number in history.Charles Longino refers to it as apocalyptic demography or the demographic imperative (2005, p. 80).There are growing concerns that the baby boomers could bankrupt the social support systems as they stand now.While all of this is taking place, older people are staying healthier and living longer than ever before.In addition to references to Nelson and Butler, this paper also refers to two recent psychology research papers which challenge previously held theories on aging, memory, and learning.Finally, it suggests a Friereian educational lens through which baby boomers could look to seek a significant role for themselves in raising the awareness of ageism as a form of oppression and to create the means to reverse its effects on everyone affected by it, not just older persons.In this paper I will be discussing several issues related to ageism.Certain defined age brackets will be assigned to terms and expressions.For instance, I will refer to people over the age of 65 to 74 as 'seniors', and people over 75 to as 'elders' and 'the elderly'.The terms 'baby boom' and 'baby boomers' will refer to the 20 year period following the end of World War II and those members of North American society born during that time.Although I consider the two age brackets to be totally stereotypical and arbitrary categorizations, and the 'baby boom' time frame to vary slightly between American and Canadian contexts, they have generally been accepted in popular use by government, the private sector, and the public at large.Keywords ageism, baby boom, demographic imperative, lifelong learning "The baby boom is on our doorstep.Is this the calm before the storm?We fear that it will be like a wrecking ball unleashing its destructive potential.However, it may be more like a challenge.Challenges can generate creative answers, many of which are unknowable ahead of time . . .We should embrace the challenge . . .(Charles Longino, Jr., The Future of Ageism: Baby Boomers at the Doorstep, 2005, p.83)Although prejudice based on age can be experienced by a member of any age group, I will be concentrating in this paper on ageism as it applies to the aging adult population, and in particular to those of the cohort referred to as the baby boom
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.135 | 0.032 |
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".