Bibliographic record
Abstract
This book is the culmination of two years of efforts involving many individuals and institutions.I would like to highlight three groups and offer them my thanks and gratitude: the authors and co-authors of this volume, the staff of Edward Elgar Publishing and the two families to which I belong, my own and the one at McGill University.I am deeply grateful for the efforts of about 24 authors and co-authors from 17 countries involved in this volume.They fall in two different groupings: a small group that I invited to write a chapter for this volume and those who submitted a paper for publication to the McGill International Entrepreneurship (MIE) Conference.There is a long and winding road from a new and innovative submission to an MIE-related publication.MIE conferences encourage exploratory, innovative and path-breaking submissions.Traditionally, a submission involves a double-blind peer review and a revision is required before acceptance for presentation at an annual conference.The conference is organized and conducted in a similar manner to a research workshop, involving only a limited number of presentations, to foster deeper understanding and discussion and possibly a convergence on innovative and cutting-edge issues, or topics at hand.The authors are asked to incorporate the essence of comments, feedback and scholarly discussions of their conference papers in their revision, through a process known in the MIE community as "self-improvement revision".Such revised and returned papers are then peer-reviewed in a double-blind fashion and a revision is requested.Once a revised paper is accepted, the early version of a chapter is born, and only then, the thorough editorial process of Edward Elgar Publishing could start.As you may have guessed, the second group to whom I am indebted and also grateful for their professional editorial efforts is Edward Elgar Publishing.The partnership of MIE and Edward Elgar is not accidental -Elgar also seeks and publishes cutting edge materials.Due to the unselfish efforts of an excellent team of editors and professionals in a quality publishing house, readers experience no difficulty in reading the materials, and authors and editors face no major problem as the manuscript travels smoothly through the numerous stages in preparation for publication.Publishing, regardless of the form and milieu, is always riddled with prob-
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.322 | 0.292 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".