Bibliographic record
Abstract
At a recent rally, Donald Trump resumed a habit he had developed during his election-rallies and read out the lyrics to a song. It tells the Aesopian fable of The Farmer and the Snake: A half frozen snake is taken in by a kind-hearted person but bites them the moment it is revived. Trump tells the fable to make a point about Islamic immigrants and undocumented immigrants from Southern and Central America: He claims the immigrants will cause problems and much stricter immigration-policies are needed.
 I assume that Trump treats the fable as an argumentative device for supporting his stance on immigration. He uses it as a source-analogue both for the conclusion that immigrants will cause problems and for changing the frame in which immigrants and those willing to let them enter are seen. This gives me opportunity to examine the effect fables have as argumentative devices. Fables are a popular and effective choice for political argumentation. They are slimmed down, semi-abstract narratives, well suited for directing the audience's attention to a few properties of an otherwise complex situation. However, this also makes it easy to use them for manipulating an audience into oversimplifying complex contexts and stereotyping human beings.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".