Bibliographic record
Abstract
I’m sure everyone’s mother, like mine, at some point said, “If you can’t say something nice, don’t say anything at all.” No doubt this was sound advice when you are 12, but as an adult and an information professional, it takes on a whole new meaning for those who blog, tweet, and otherwise comment on critical issues impacting their work and workplace communities. You have opinions, and potentially concerns about issues that you want to share with others who might be dealing with similar issues. Sometimes as part of those commentaries, it becomes necessary to not restrict yourself to remaining “nice” in order to communicate your thoughts, beliefs and perspectives about the practices or actions of others Two librarians who blog about publisher’s issues recently learned about the potential costs that are associated with not necessarily remaining “nice.” Dale Askey, a librarian at McMaster University in Ontario, Canada was sued by the Edwin Mellen Press and its founder, Herbert Richardson for a 2010 blog entry that was critical of the Press. The suits included both Mr. Askey and McMaster University and claimed that the blog entry defamed the Press and its founder. The lawsuits sought damages of over $4 million for the defamation. More recently, Jeffrey Beall, a librarian at the Auraria Library at the University of Colorado Denver received a letter from lawyers representing the Canadian Center for Science and Education, claiming that a series of blog entries posted by Mr. Beall defamed the Center and some of its related publications. The letter demanded that Mr. Beall “immediately remove” the offending posts and demanded an immediate payment of $10,000 for “attorneys fees and damages.” Failing to comply, the letter implied, would result in a lawsuit.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".