Functional Collaboration as the Implementation of Lonergan's Method, Part 2: “How Might We Implement Functional Collaboration?”
Bibliographic record
Abstract
In the first part of this paper, I discussed the problem for which functional specialization is a solution. 1As we discovered, the 'problem' involves a complex set of axial issues.For Lonergan, it was most evident in the need to revitalize the out-of-date theological method he encountered as a professor of theology in Canada and Rome.Over the last couple of centuries developments in the sciences, their methodology, and practical applications-in mathematics, in historical methods, in existential philosophy, in depth psychology, and so forth-had all significantly challenged the classical traditions.The choice, however, is not between preserving traditions and embracing innovations.As Lonergan expressed it: "What will count is a perhaps not numerous center, big enough to be at home in both the old and new, painstaking enough to work out one by one the transitions to be made, strong enough to refuse half measures and insist on complete solutions even though it has to wait." 2 The 'not numerous center' will be the effective zone of functional collaboration.The challenge is to figure out together how we might effectively implement theoretical, scientific developments, including advances in our understanding of human interiority, to
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".