R&D Intensity and the Value of Analysts’ Recommendations*
Bibliographic record
Abstract
Contemporary Accounting ResearchVolume 29, Issue 2 p. 621-654 R&D Intensity and the Value of Analysts’ Recommendations* DAN PALMON, DAN PALMON Rutgers UniversitySearch for more papers by this authorARI YEZEGEL, ARI YEZEGEL Bentley UniversitySearch for more papers by this author DAN PALMON, DAN PALMON Rutgers UniversitySearch for more papers by this authorARI YEZEGEL, ARI YEZEGEL Bentley UniversitySearch for more papers by this author First published: 25 June 2011 https://doi.org/10.1111/j.1911-3846.2011.01117.xCitations: 30 † Accepted by Jeffrey Callen. We would like to thank Sudipta Basu (discussant), Jeffrey Callen (associate editor), Alia Crocker, Rani Hoitash, Pyungkyung Kang, Ann Medinets, Bharat Sarath, Ephraim F. Sudit, the anonymous referees of this Journal, and seminar participants at Bentley University, Lehigh University, Penn State at Great Valley, and the University of Delaware for their valuable comments and suggestions. We also benefited from comments of participants at the American Accounting Association 2008 Annual Meeting and American Accounting Association 2009 Northeast Region Meeting. This research was supported in part by a Faculty Research Grant from Rutgers Business School—Newark and New Brunswick. Ari Yezegel acknowledges the generous financial support provided by Bentley University through the FAC grant. All errors are the authors’ responsibility. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume29, Issue2Summer 2012 (June)Pages 621-654 RelatedInformation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".