Continuing an Epoch: Examining the Necessity of the Field of Political Science
Bibliographic record
Abstract
In 2008, the United States of America elected its first African-American President, Senator Barack Obama. This seminal election was viewed upon as a pivotal point of heightened student interest and involvement in United States electoral politics. With the re-election of President Barack Obama in 2012, the student involvement and voter participation was again heeded as an exemplification of increased student interest in U.S. Electoral politics. To examine the validity of these viewpoints, the authors put forth that there should exist some metrics to demonstrate the growing student interest in U.S. Electoral Politics. As such, this article bears two purposes: 1) to examine the assumptions of a growing student interest in U.S. electoral politics from the perspective of actual political science enrollment totals, nationally and at one local university and 2) to discuss the potential implications of the data and make recommendations as to its usefulness for the local university being studied in this research project, as well as the field of political science. Data presented in this study will be taken from the local university studied, based upon a twelve (12) year (2003-2014) reporting period of political science student enrollment, at the Bachelor’s and Master’s level. Additionally, student enrollment data at the Bachelor’s and Master’s level in the School of Public Policy and Administration will be presented to illustrate an apparent trend contrast occurring within these two programs, at this particular university. The 12-year reporting period is based upon the availability of public data at the reporting institution.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".