To Authenticate Opinions and Redraw the Legitimate Boundaries of the Public Sphere
Bibliographic record
Abstract
Short outline of my project: The dissertation aims to explore the explanatory potential of the concept of authenticity to identify successful introductions of topics previously considered as beyond public deliberation from 2005 and onwards. My main focus is religious claims in the Danish public sphere. My theoretical and methodological approaches are closely interlinked and my presentation therefore deals with both theoretical and methodological strengths and weaknesses. Ethics of Authenticity The basic concept of my dissertation is the concept of authenticity. The concept has a long history in existentialist philosophy. Among the many prominent interlocutors are the Danish philosopher Søren Kierkegaard and the French existentialist philosopher Jean-Paul Sartre (Kierkegaard 2006, ch. 1; Sartre 2003). Today, however, one of the most influential conceptualisation of authenticity has been outlined by the Canadian philosopher Charles Taylor (1989, 1992). Charles Taylor defines the concept of authenticity as being true to oneself within so-called horizons of meaning that makes some features of life seem worthwhile in comparison to others. The term horizon of meaning is defined as a background of intelligibility against which things take on importance (Taylor 1992: 33, 37). This definition is based on the assumption that the identity of each individual is dialogically constructed in relation with
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".