Bibliographic record
Abstract
ith so many issues deserving of scrutiny in the work of John V V Caputo, both in his essay "There are no truths only texts" which directly occasioned this response and most certainly in his rich corpus as a whole, it is difficult to know where to begin.And this task is made even more difficult when one finds oneself in basic agreement with so much of Caputo's thought, for example, his claim that Der rida's works can and ought to be defended against the charges of "rela tivism, scepticism, irrationalism and nihilism" (Caputo 2003, 2).As a hermeneutic clue to investigate Caputo's work, I have chosen a theme that may seem quite distant from the topics of religion and ethics that have dominated his most recent texts: namely, phenomenology.In deed, it may seem an even more unlikely way to approach the writings of Jack Caputo, because one reading of his philosophical corpus does detect a marked and increasing "marginalization" of phenomenology in his work.But if Derrida has taught us anything-it is that "margin alization" is itself a most fascinating phenomenon-that what is mar ginal is never exactly what it first appears to be; and that it is the mar gins themselves that form the conditions of possibility for what we take to be "central" in the work of an author.And so I want to play in this marginal space of phenomenology.I wish to speak "of phenome nology," which in its Husserlian variety provides the entrance into phi-
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".