Bibliographic record
Abstract
Collected here for the first time, this series of lectures delivered by Lonergan at Boston College in 1957 illustrates a pivotal time in Lonergan's intellectual history, marking both the transition from the faculty psychology still present in his work Insight to intentionality analysis and his initial differentiation of the existential level of consciousness. The lectures on logic deal with the general character of mathematical logic and its relation to truth, Scholasticism, and Aristotelian logic. Continuing Lonergan's long-standing interest in the foundations of thought, the lectures on existentialism offer a penetrating account of Husserl and his influence. They also deal with Jaspers, Heidegger, Sartre, and Marcel. They offer reflections on such topics as being oneself, dread, horizon, and the existential gap. Perhaps more dramatically than in any other work these papers reveal Lonergan's dual commitment to the rigor of scientific analysis (in the field of mathematical logic) and to the sensitivity of continental philosophies to existential issues. Bernard Lonergan (1904-1984), a professor of theology, taught at Regis College, Harvard University, and Boston College. An established author known for his Insight and Method in Theology, Lonergan received numerous honorary doctorates, was a Companion of the Order of Canada in 1971 and was named as an original members of the International Theological Commission by Pope Paul VI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".