REFLECTIONS FROM THE FRONT LINE Bernard Corry Memorial Lecture
Bibliographic record
Abstract
Thank you to Queen Mary College and especially Jonathan Haskel for inviting me to give this lecture. It’s strange to give a lecture in the name of your dad – but also an honour. I was never sure when I was young what exactly my dad did. When I was young he taught at LSE and I was convinced he was at the London School of eco-Comics – which sounded pretty good to me. But when I visited the office I found a distinct lack of the Beano, or Scorcher and Score. And my main early memories of QMC I’m afraid are not of breakthrough economics but that it seemed a long way off from south west London where we lived. We used to disagree a fair bit on economics – as well as politics. In my very youthful days I of course saw his Keynesian bias as a sell out to capitalism and was more interested in the more socialist policies being advocated by the Conference of Socialist Economics and its stars, like another alumnus of this place, David, now – naturally- Lord, Currie. Later, while doing my Masters at Queens ’ University in Canada under people like Doug Purvis and working as teaching assistant for John Driffill, another ex QMC professor, I became pretty enamoured by the lure and elegance of the new classical macro economics. I remember my relief at discovering that you only had to put some staggered labour market contracts in to
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".