ORCID AND OTHER ACADEMIC SOCIAL ACCOUNTS OF A CANADIAN ECONOMIST PROMINENT IN THE MEDIA
Bibliographic record
Abstract
In a bid to disambiguate authors’ names, ORCID (Open Researcher and Contributor ID) was created in 2012 as a bold initiative, and has been increasingly used by academic publishers to identify authors. On this platform, author’s have the freedom to maintain the accuracy of their academic record. ORCID profiles that are incomplete defeat the purpose of that system, since inaccuracies cast doubt on the information therein. In contrast, complete and up-to-date ORCIDs benefit authors and the academic community as they act as tools of transparency and verification. It is in the interest of authors to maintain their ORCID profiles complete, accurate and up to date. In this paper, the public ORCID profile of a Canadian economics academic (Derek Pyne; http://orcid.org/0000-0003-0849-0895 ) with a high media profile and a modest publishing profile (i.e., less than 30 published papers throughout his entire career), was examined. Similarities and differences relative to seven other profiles for this academic (institutional, Google Scholar, Ideas/RePEc, Mendeley, Scopus Author ID, ResearcherID, KUDOS) are highlighted. This case study sheds important information that will allow economists and academics in other fields to reflect on the use of ORCID and other academic profiles.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.030 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".