Bibliographic record
Abstract
The dates on my initial computer files remind me that I first developed the detailed argument and structure of this book in April 2001. I remember being concerned at the time that the book might take two years to write and publish – but I wanted it out more quickly than that. Such optimism. Various commitments intervened. One was a book on costing greenhouse gas abatement policy that I wrote and published with two co-authors in the period 2001–02. There was also international and national advisory work related to climate change policy as well as my ongoing research, article writing and teaching responsibilities. If a manuscript is incomplete by the end of summer, there is little chance for significant progress during the following eight months of teaching. I finally had a virtually completed draft of the manuscript by mid-2004, but then I consumed almost a year eliciting feedback from various researchers, especially in areas that are peripheral to my expertise. This led to more research and significant rewriting in some sections. I am grateful to the many people who provided this service. Of course, I alone am responsible for remaining errors. It is difficult to anticipate how this book will be received. I have always seen myself as strongly motivated by a concern for the environment, so I am predisposed to arguments that our economic system is unsustainable and that our policies for environmental protection need to be much stronger. I hope that message is clear in this book.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.550 | 0.376 |
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".