Bibliographic record
Abstract
In today's ultra-competitive global economy, intangibles are increasingly taking centre stage in firms' business strategies and investors' valuations. Physical and financial assets are becoming commodities, yielding at best a competitive return on investment. In their place, intangible assets such as patents, brands, unique business processes, breakthrough scientific discoveries, and strategic alliances are what firms are using to create dominant market positions, control risk, generate abnormal profits, and achieve growth and wealth. The dramatic rise and fall of high-technology company valuations over the past five years has brought the unusual economic characteristics of intangible assets into the public arena. The concurrent advantages and vulnerabilities of intangible-intensive companies has highlighted the importance of having an in-depth understanding of the economics of intangibles and developing tools to better manage and evaluate them. This Reader provides that understanding by bringing together the best research and advocacy on intangibles. The chapters provide a comprehensive tableau of both rigorous perspectives and empirical evidence about intangible assets by scholars and policy makers in accounting, economics, finance, and information technology. As such, the Reader both informs and sets a solid foundation for the next generation of challenging questions that need to be addressed. The Reader has four sections: Section I explains why intangibles have become so important in the modern economy. Section II investigates the impact of specific kinds of intangibles on firm performance and equity market values. Section III documents the severe adverse effects of the informational deficiencies that are created by the accounting and financial reporting rules that govern intangibles. Finally, the chapters in Section IV call for improved disclosure and measurement of intangibles in financial statements, and make concrete suggestions for what such solutions should look like. Contributors to this volume - David Aboody (The Anderson School, UCLA) Mary Barth (Stanford Graduate School of Business) Margaret Blair (Georgetown University Law Center) Stephen Bond (Nuffield College, Oxford) Jeff Boone (Mississippi State University) Louis Chan (University of Illinois) Michael Clement (University of Texas at Austin) Jason Cummins (Federal Reserve Board) Michael Darby (The Anderson School, UCLA) Z. Deng (Ph.D. student, New York University) George Foster (Stanford Graduate School of Business) John R. M. Hand (Kenan-Flagler Business School, UNC Chapel Hill) Ron Kasznik (Stanford Graduate School of Business) Josef Lakonishok (University of Illinois) Baruch Lev (Stern School of Business, New York University) Joan Luft (Michigan State University) Randall Morck (University of Alberta) Leonard Nakamura (Federal Reserve Bank of Philadelphia) Francis Narin (President, CHI Research) K. K. Raman (University of North Texas) Paul Romer (Stanford Graduate School of Business) Chandra Seethamraju (Olin School of Business, University of Washington, St. Louis) Carl Shairo (Walter A. Haas School of Business, University of California at Berkeley) Michael Shields (Michigan State University) Theodore Sougiannis (University of Illinois) Hal Varian (University of California at Berkeley) Steven Wallman (Founder and CEO of FOLIOfn) Bernard Yeung (Stern School of Business, New York University) Paul Zarowin (Stern School of Business, New York University) Lynne Zucker (UCLA)
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".