Solving the Contact Paradox: Rational Belief in the Teeth of the Evidence
Bibliographic record
Abstract
Evidentialism is the doctrine that rational belief should be proportioned to one’s evidence. By “one’s evidence,” I mean evidence that we possess and know that we possess. I specifically exclude from “evidence” the following: information of which we are unaware that our brain might rely on in constructing experience or in the formation of beliefs. My initial interest is with the doctrine of Evidentialism as it applies to a quandary that arises in the Sci-Fi movie Contact, the “Contact Paradox” as I will call it. In this movie one of the main characters, Ellie, is a cosmologist working in a radio-telescope research facility searching for signals from intelligent life in the cosmos. The entity whose epistemological status is at issue in her quandary is her deceased father but there is an obvious parallel between the quandary of a rational believer in God and Ellie’s quandary, a parallel extensively explored in the movie itself. My first thesis is that in Ellie’s case Evidentialism is false: in certain cases, it is rational to believe in the existence of an entity in spite of the fact that the empirical evidence overall is contrary, and the Contact Paradox is one such case. Later in the paper I turn attention to the issue of Evidentialism regarding beliefs in the existence of God. My second thesis is that Evidentialism is false there as well.
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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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".