Current Copyright Law and Fair Use: The Council of Editors of Learned Journals, Keynote Address, MLA Convention 2000
Bibliographic record
Abstract
For its keynote address at the 2000 MLA Convention, the Council of Editors of Learned Journals asked two experts in the area of publishing and copyright law to update editors and publishers about issues of copyright and fair use facing us in the digital age. Robert Spoo, a former academic journal editor and now a lawyer specializing in intellectual property law, warns that editors must be more mindful than ever, in light of new forms of digital publishing and republishing of scholarly work, to take care of their professional interests. He examines some problems we can expect to confront as the analogue world is further ‘consumed’ by the digital world. The doctrine of fair use must be intelligently applied to support scholarly interests. Harold Orlans, a long-time observer of the publishing world as editor of The Independent Scholar and a columnist for Change, the bimonthly magazine of the American Association for Higher Education, responds to Spoo's presentation and suggests how those in the scholarly publishing field might effectively stand behind fair use by providing sound directives for both upholding fair use and reducing the growing chaos surrounding copyright permissions. Both Spoo and Orlans would like to see scholarly editors and publishers avoid timidity before the law and instead lead in the area of copyright and fair use.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.036 | 0.020 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.029 | 0.021 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".