Bibliographic record
Abstract
I: IntroductionTraditional notions of liberal, democratic politics1 and freedoms fostered in the United States and found in documents like the “Declaration of Independence” and “Constitution” have relied on the ideas of an open, rich marketplace of political ideas, individual assertions of rights, energetic public spaces and dialogue, and clear limitations of governmental powers. As American as Walt Disney World (WDW) is, it smoothly suppresses these ideas, even where it presents utopian visions of the country’s past, present, and future.2 Visitors to WDW bask in the feeling of being “freed up” from the demands of their daily lives3 as they move through a highly controlled and controlling environment. Public spaces here are really private, corporate avenues to a limited variety of rides, food, shows, stories; they also serve as stages for carefully orchestrated performances by “cast members.“ Individuals readily fall into lines, rarely move their conversations beyond their own family or group, and take photos at the anointed “Kodak Picture Spots“ around EPCOT (Klugman 15). The pleasure here lies largely in the clean, organized, commercial lack of freedom.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".