Forever productive: The discursive shaping of later life workers in contemporary Canadian newspapers
Bibliographic record
Abstract
Increasingly, ;productive aging' is promoted within government policies and reports in several Western nations, as well as those of international organizations. The ways in which ;productive aging' comes to be shaped within texts, that is, its discursive shaping, influences what aging individuals view as possible and ideal ways to be and do in later life, as well as what collectivities view as required services and programs to support such identities and occupations. Drawing on governmentality theory, in concert with occupational science, a critical discourse analysis of 72 Canadian newspaper articles pertaining to work and retirement published in 2006 was conducted to examine how 'productive aging' is shaped within such print media texts and the possibilities for identity and occupation promoted. This work critically analyzes ways 'later life workers' have come to be discursively shaped within neoliberal sociopolitical contexts, characterized by emphases on fostering individual responsibility, decreasing state dependency, and increasing privatization. The authors raises concerns related to occupational injustice, arguing for continuing vigilance regarding the ways 'productive aging' discourses might be drawn on to justify further state and workplace retreat from policies and programs that support those who face challenges to continued engagement in work or who cannot, or chose not to, be 'forever productive'.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.030 | 0.020 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".