Convergent Knowing: Explorations of a Sustained - and 'Sustainable' - Theological Reflection on Science, Environment, and Liberation
Bibliographic record
Abstract
Since the last half of the twentieth century, Christian theorists, theologians, biblical scholars, philosophers and ethicists have been struggling, in varying degrees, with the increasingly pressing issues that have arisen from a planetary environmental crisis and a growing inequality and persistent poverty afflicting the majority of human beings. They have tried to reconcile these emergences with discoveries from science about how our world functions, and traditional affirmations of their faith. The struggle has been daunting, marked by a litany of concerned voices that argue that one or more of the issues or facets of the debate require greater attention. In this dissertation, I investigate the writings of four Christian thinkers: Rosemary Radford Ruether, Leonardo Boff, Diarmuid O'Murchu and Thomas Berry. These authors attempt to integrate, to varying extents, environmental concerns, liberation thought, scientific discovery and traditional precepts of their faith. These thinkers, I argue, are greatly facilitated in their integration of all these aspects by the particular epistemic framework they employ when engaging their faith and science in a communal conversation. I label this framework "convergent knowing," characterized by a close and seemingly continuous relationship between these two significant ways of knowing. I suggest that "convergent knowing" could serve as a model for other Christian thinkers who are currently struggling to integrate ecology, justice, science and their faith. Evidence for this is found within the collective work of the four authors I examine, which reveals an emerging ethical vision that seeks liberation for the human and the other-than-human. I further argue that "convergent knowing" adds a new and important dimension to the religion-science debate, one that does not seem to be adequately represented by current leading typologies representing the religion-science nexus. The dissertation concludes by suggesting that there might be a larger civilizational paradigm shift occurring that underlies a growing convergence of Christianity and science. Characterized by relationality, this new paradigm is shaping how scholars currently approach science, ecology, ethics and religion.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.091 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".