Reading the (identity politics) market: Articulating the forest past the trees post-Trump
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prior to becoming the President-elect, Donald Trump long engaged in the practice of exploiting economic trends that displayed a potential for increased rates of profit maximization. Like those engaged in speculative investment, he looked for exploitable opportunities where a modest outlay could be directed toward a precise stream of the market with the sole intent of receiving an exacerbated rate of return compared to the allotment initially invested. Over the past decade, the United States has witnessed a unique political climate of a disorganized, yet growing, movement of frustrated citizens inarticulately moving to the Right. It could be argued that Trump saw a prospective market ripe for exploitation herein, which showed a very real potential for significant returns. Without a centralized focus or guide, these under-formed sociopolitical blocs traversing the country were thus read as a vulnerable venture. It was amidst this climate that a capitalist with a speculative eye looked at a prospective rising market that could provide one chance investor an impressive yield: the US Presidency. By adopting a unique performativity, Trump invested in 2015.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it