Bibliographic record
Abstract
It was through a chance telephone conversation in 1989 that we forged the research collaboration that has led to many joint scholarly papers and, now, to this book.We share in the belief that there are considerable benefits to be gained by paying close attention to the words of business leaders and, in particular, to performing a detailed analysis of what ceos of large corporations write about financial matters.A book such as this would not have been possible without the encouragement and practical assistance of many institutions, colleagues, librarians, relatives, and friends.Joel Amernic acknowledges the collegial academic environment provided by the University of Toronto's Rotman School of Management, the research support of the Canadian Academic Accounting Association, and the intellectual stimulation provided by colleagues, reviewers, and editors over many years.Russell Craig recognizes the support of the Australian National University, especially its Outside Studies Program.He acknowledges the assistance of the International Commission for Canadian Studies through a Canadian Studies Award and also thanks Canadian National Railway for providing materials that greatly facilitated the completion of chapter 11.Both authors are grateful to Mary Williams, copy editor, for many constructive suggestions.In some ways this book is a reflective overview of a broad body of work we have conducted over the past decade.Some parts,
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.308 | 0.202 |
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".