Toward a Dialogical Hermeneutic of a Hindu-Christian: A Socio-scientific Study of Nepali Immigrants in Toronto
Bibliographic record
Abstract
In search of a hermeneutic that is dialogical, transcending one’s own realm of understanding to give enough space to the other, the theory of dialogical self provides a framework which is not only able to engage mutually incompatible traditions but inculcates a whole new insight into considering that the other is not completely external to the self. One of the most significant features of theory of dialogical self is that it is devised in the conviction that insight into the workings of the human self requires cross-fertilization between different fields. The thesis therefore employs social-psychology, religious studies, inter-cultural studies, theology and philosophy to study the phenomenon of religious diversity. Within this theoretical framework, the thesis includes an empirical study conducted among Hindu Nepalis in Toronto, analyzing their encounter with people of other religious traditions and their attitudes towards them. Complementing the empirical analysis is Panikkar’s Cosmotheandric vision which functions on the premise that the whole of reality is integrated – cosmos, theos and anthropos. This paradigm helps to explain religious diversity and combined with the insights learned from the empirical research illustrates how the other is indispensible in dialogue. This thesis concludes with an elaboration of a dialogical hermeneutic of a Hindu-Christian.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".