Author Addendum Conundrum: Reconciling Author Use of Addenda With Publisher Acceptance
Bibliographic record
Abstract
The purpose of this paper is simultaneously to investigate researcher use and awareness of author addenda (e.g., the Scholarly Publishing and Academic Resources Coalition [SPARC] author addendum) and publisher awareness and acceptance of the same. Researchers at U15 Group of Canadian Research Universities institutions were targeted, and a survey was sent to faculty, graduate, and postdoctoral associations to share with their members. Following a low response rate, the survey was sent to a listserv of copyright librarians in Canada with a message that encouraged them to share it with researchers at their institutions. Eighty-one researchers responded to the survey. Eighty-six percent of researchers (n = 70) indicated that they were unaware of author addenda. Researchers were asked to identify how often they negotiate their publishing agreements, and of those who answered the question, 84.2% (n = 64) responded that they never negotiate. Thirteen publishers or publishing organizations were contacted and asked if they would participate in phone interviews about copyright practices and author addenda. Two large multinational publishers agreed to participate. Both publishers indicated that very few authors attempt to negotiate their agreements and that of those who choose to negotiate, even fewer use addenda. Both indicated that they do not accept the SPARC author addendum. This study’s small sample sizes mean that more information needs to be collected before firm conclusions can be drawn. Based on the responses from the two large publishers, the best way to help Tri-Agency-funded researchers may be for libraries and the Tri-Agency to negotiate with publishers for funder-based exceptions.
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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 | 0.157 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".