Bibliographic record
Abstract
Frequent occult or conspiracy circles longenough—especially those centered around theparanormal and ufology—one begins to notice atrend. UFO sightings or alien abductions, fairfolk conducting séances, leprechaunsfrantically hiding their coveted gold, and otherodd occurrences, are seldom happenings foundin populated areas. Indeed, for the skeptic, thefact of isolation with a lack of witnesses is thesingle most powerful weapon in their arsenal.“If such-and-such event really did occur, whyare there no witnesses? Why did it happen inthe abandoned church? Why do all yoursightings happen in the most remote oflocations?” she asks. The secluded, hiddenlocations of these events is not happenstance,however. It is not a tool to explain awayanomalies. Rather, these things must occur insecluded, run-down areas because secludedlocations are thresholds between the world ofappearances and the world of things as theyare. They are areas where the supposedly‘hard,’ ‘natural,’ and ‘immutable’ boundaries ofthe world break down. They are the wavewracked shores of Kant’s Island of Reason, histerra firma slowly being eroded.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".