Bibliographic record
Abstract
The idea for this journal developed out of discussions among participants at a series of Lonergan Conferences held in Nova Scotia, Canada in 1997, 1999 and 2000.The first conference, coinciding with the publication of Lonergan's For a New Political Economy, introduced Lonergan's macroeconomic dynamics in a series of workshop sessions presented by Philip McShane.The second conference expanded the context of the first meeting, exploring the relevance of macroeconomic dynamics to core issues of social justice.Some things became clear: first, that macroeconomic dynamics challenged the root assumptions of present day economic analysis; second, that the probability at this time for gaining a sympathetic hearing for macroeconomic dynamics in mainstream economic journals was slim; and third, that issues of economic justice involved us in a series of questions about the practical implementation of the theoretic discoveries that went beyond economics properly speaking.We recognised that a fruitful forum for discussion of macroeconomic dynamics needs to explicitly incorporate developments in the notion of science in the light of generalized empirical method and functional specialization.The theme of the third conference, "Creating Categorial Characters," brought home to participants the long-term personal and collective challenge of displacing prevailing methods and approaches in the academy.We acknowledged that the inclusion of methodological questions opened up the possibility of a journal addressing issues that pertained to the implications of macrodynamic analysis not only for economics
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".