Social policy language in the United States
Bibliographic record
Abstract
Policy discourse in the United States (US) has drawn on languages about labour, citizenship, and family. Beyond the specifications of formal citizenship (birth in the US, naturalisation procedures, freedom and unfreedom, voting rights), there is a language that shapes understandings of citizenship – rights and obligations, membership or inclusion, and individuals’ relationship to the state. From the mid-19th century, industrial capitalism sparked mass migration and immigration, intensive urbanisation, wage work, and the lack of work. The rapidly changing nature of American democracy, historian Michael Katz noted, also brought with it new questions about who merited help and what the limits of social obligation would be (Katz, 2001: 341). The terms and concepts used became a constitutive element in US social policy language. Yet social policy is also a story of contestation and, in this respect, language and discourse matter. ‘The substance of American politics,’ finds Gary Gerstle, ‘changed dramatically over time as different groups gained and then lost control of [an articulated political] language …’ (Gerstle, 1989: 9). The development of US social policy language was not only about ideology and party politics. First, since the late 19th century, there has been a persistent tension between the charity reform conception of the ‘deserving’ and the ‘undeserving’ and the labour/industrial reformers’ concerns about the social and health hazards of industrialism and protecting the wage earner and wage income. Bridging family and market, the language of social policy reflected (and indeed intended to shape) the expected gender roles of workers, nonworkers/dependants, and citizens. The Progressive Era (1880s–1910s) rhetoric of the family wage for a male breadwinner seeped into all policy language. From the incipient moments of industrial era social policy, welfare advocates sought to separate motherhood from ‘breadwinning’. Public relief was supposed to ‘honour motherhood’ and support it; yet in every era, there has been a resentment that poor people were not seeking and maintaining employment in the paid labour force. Questions of citizenship related to race and immigration were ever present. How did the language of social policy reconfigure citizenship to include those outside its boundaries (women, African-Americans, immigrants), and by the late 20th century, to exclude others and thereby reconfigure citizenship once again?
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".