Bibliographic record
Abstract
Words have profound power to shape our world, influencing our ideas, relations, and interactions with people around us.The language of news outlets, social media, everyday speech, and even educational and health care messaging is rife with ageist rhetoric -this fact is well-known to us in age studies.Many researchers in the field are (and have been for some time) critically engaging with the question of language in important ways, and challenging ageist rhetoric is a core objective.However, even within age studies, differences in language use and notions of what is productive (or even acceptable), exist.Additionally, language, terminology, and the meaning of words are not static.In a field as interdisciplinary as age studies, the specific use of terminology is also not homogenous.For these reasons, paying close attention to the precise ways in which language use shapes broader images of age identities forms an essential part of age studies research.This forum on Contested Language was first inspired by North American Network in Aging Studies (NANAS) Governing Council meetings during which we noticed that questions of language re-surfaced frequently.The council members, all researchers with a fair amount of exposure to and experience with age studies or cultural gerontology, still used certain words differently, preferred differing language choices, or had different opinions about the degree to which certain terms were acceptable or not, based on, for example, discipline, region, culture, or tradition.NANAS's stated mission is to "facilitate sustainable, international and multi-disciplinary collaboration among all researchers interested in the study of cultural aging" ("Guiding Principles of NANAS").One of its guiding principles is explicit about the importance of language, stating the aim to "[s]upport inclusive language that avoids sexist, racist, ageist and ableist terms considered to be demeaning and offensive to others."As an age studies organization, reflecting on our own mission and guiding principles, we recognized the need to pay ongoing critical attention to the language concerning age and aging, especially now as occasioned by the COVID-19 pandemic and responses to it.We also acknowledged that
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".