Hoosiers, Holcomb, and the Landslide: The 2016 Indiana Gubernatorial Results
Bibliographic record
Abstract
This research paper assignment on the 2016 Indiana gubernatorial election was done in two parts. The first part (not included) was on predicting the winner of this election. My prediction for the Democratic candidate, John Gregg, was incorrect. This essay is the follow-up analysis (written within two weeks after the election results) of what transpired during the election in relation to my prediction. I argue that the Eric Holcomb win and Gregg loss was due to the larger national Republican victory, a lack of Democratic campaigning power in Indiana, Gregg’s (over)emphasis of LGBT rights, his dismay for the RFRA, and Holcomb’s optimistic economic plans. Gregg’s connection to Mike Pence is also discussed as a defining factor of his win. Demographics of the Indiana exit polls are also considered. While the race was polled, and reported as a toss-up in the weeks before the election, in hindsight, a Republican Governor winning in a largely Republican state is a logical conclusion.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".