Heidegger Uncovered. An Encounter with: Mark A. Wrathall, <i>Heidegger and Unconcealment: Truth, Language, and History</i>
Bibliographic record
Abstract
Truth, Language, and History, focuses on Heidegger's idea of alêtheia (Unverborgenheit) and its relation to the opening up of the world, the disclosure of beings, and the uncovering of entities in order to challenge multiple debates in contemporary analytic philosophy on the questions of truth, language, and history.However, these ten essays-two previously unpublished, and all of which span the last ten years of Wrathall's engagement with Heidegger-are more than just a handy resource for those analytic philosophers looking to engage with the methodological principle of unconcealment.What is of equal if not greater importance for Wrathall is to show how and why a failure to understand Heidegger's idiosyncratic and ontologically broad use of terms leads to terrible errors-concealing what Heidegger has to say.Wrathall poses some deep criticisms of entrenched mistakes in the literature on Heidegger, especially with regards to his later work.For example, a failure to realize that Heidegger was using the word "truth" in a broad sense has damaged his account of unconcealment and its relationship to truth.To be sure, Heidegger did use truth as a name for unconcealment; however, it is wrong to assume that by truth, Heidegger was referring to propositional truth or that propositional truth could be defined as unconcealment.Throughout all the essays, Wrathall hones in on misunderstandings such as these, which he compellingly shows to pervade much of the
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".