Bibliographic record
Abstract
The contributions of Marc Lavoie and Mario Seccareccia to macroeconomics are many, important and various. These contributions extend beyond their own research to their leadership in fostering others’ research. They have exercised this leadership, not only through their editing activities (most recently Lavoie and Seccareccia, 2017), but also in creating a supportive and productive academic environment at the University of Ottawa. I benefited from this myself first in 1983 when I was invited to present a seminar there. For the first time, I encountered impatience with what was then the required convention for heterodox economists of discussing the mainstream account of a topic before moving on to the heterodox alternative. This was a liberating experience. Lavoie and Seccareccia share a fundamental concern to tailor economic analysis to addressing pressing socio-economic problems. Rather than being constrained, as is typical among mainstream economists, by internal methodological concerns, Lavoie and Seccareccia have pursued methodologies according to external methodological concerns, choosing whatever best suits the real policy problem at hand. They are methodological pluralists, allowing for a range of approaches to analysing a complex, evolving reality, while (as is proper for methodological pluralists) arguing strongly for the relative merits of their own chosen approach while critiquing alternatives (as in Seccareccia’s, 1988, critique of idealisation in mainstream economics, and Lavoie’s, 2018, critique of DSGE modelling). They also accordingly explore relevant developments in the history of economic thought.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".