Bibliographic record
Abstract
In this essay, I posit President Obama's hopeful promise--Yes, can!--within the framework of American liberal discourse. I examine how both the promise and the discourse within which it fits disguise the material realities of exclusion and oppression behind vague principles of freedom and equality. By parsing Obama's phrase and tracing its roots to the origins of American notions of identity, I try to show how the national collective imagined in the phrase Yes, can, is situated against assumptions of an Other--primarily African-American. I argue that such vague notions of a national collective serve not to unite, but rather to marginalize. Those who cannot or do not identify with the majority are often left out of the discussion. In this vein, I propose that arguments often made in the name of national interest--the collective we imagined in Obama's phrase-- serve many, but not all, and that such discourse is historically undergirded by an ideology of individualism and self-help, an ideology fueling current arguments against government programs benefiting the nation's poorest citizens.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".